今日学术视野(2015.9.8)

• [cs.AI]Ontology Based SMS Controller for Smart Phones
• [cs.CL]The influence of Chunking on Dependency Crossing and Distance
• [cs.CV]A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place
• [cs.CV]An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes
• [cs.CV]CNN Based Hashing for Image Retrieval
• [cs.CV]Conjugate Gradient Acceleration of Non-Linear Smoothing Filters
• [cs.CV]EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
• [cs.CV]Image Classification with Rejection using Contextual Information
• [cs.CV]Learning Temporal Alignment Uncertainty for Efficient Event Detection
• [cs.CV]Semantic Amodal Segmentation
• [cs.DC]The Anatomy of Big Data Computing
• [cs.IR]Incremental Active Opinion Learning Over a Stream of Opinionated Documents
• [cs.LG]Deep Broad Learning - Big Models for Big Data
• [cs.LG]Diffusion-KLMS Algorithm and its Performance Analysis for Non-Linear Distributed Networks
• [cs.LG]Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root Growth
• [cs.LG]Parallel and Distributed Approaches for Graph Based Semi-supervised Learning
• [cs.LG]Probabilistic Neural Network Training for Semi-Supervised Classifiers
• [cs.LG]l1-norm Penalized Orthogonal Forward Regression
• [cs.NA]Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization
• [cs.NI]Predicting SLA Violations in Real Time using Online Machine Learning
• [physics.soc-ph]Ranking nodes in growing networks: When PageRank fails
• [stat.ME]Latent drop-out transitions in quantile regression
• [stat.ME]On Combining Estimation Problems Under Quadratic Loss: A Generalization
• [stat.ME]Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal’s Fail-Safe Number
• [stat.ML]Minimum Spectral Connectivity Projection Pursuit for Unsupervised Classification 

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• [cs.AI]Giraffe: Using Deep Reinforcement Learning to Play Chess
Matthew Lai
//arxiv.org/abs/1509.01549v1 

This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Unlike previous attempts using machine learning only to perform parameter-tuning on hand-crafted evaluation functions, Giraffe’s learning system also performs automatic feature extraction and pattern recognition. The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines - all of which containing thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters. Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess. 

• [cs.AI]Ontology Based SMS Controller for Smart Phones
Mohammed A. Balubaid, Umar Manzoor
//arxiv.org/abs/1509.01379v1 

Text analysis includes lexical analysis of the text and has been widely studied and used in diverse applications. In the last decade, researchers have proposed many efficient solutions to analyze / classify large text dataset, however, analysis / classification of short text is still a challenge because 1) the data is very sparse 2) It contains noise words and 3) It is difficult to understand the syntactical structure of the text. Short Messaging Service (SMS) is a text messaging service for mobile/smart phone and this service is frequently used by all mobile users. Because of the popularity of SMS service, marketing companies nowadays are also using this service for direct marketing also known as SMS marketing.In this paper, we have proposed Ontology based SMS Controller which analyze the text message and classify it using ontology aslegitimate or spam. The proposed system has been tested on different scenarios and experimental results shows that the proposed solution is effective both in terms of efficiency and time. 

• [cs.CL]The influence of Chunking on Dependency Crossing and Distance
Qian Lu, Chunshan Xu, Haitao Liu
//arxiv.org/abs/1509.01310v1 

This paper hypothesizes that chunking plays important role in reducing dependency distance and dependency crossings. Computer simulations, when compared with natural languages,show that chunking reduces mean dependency distance (MDD) of a linear sequence of nodes (constrained by continuity or projectivity) to that of natural languages. More interestingly, chunking alone brings about less dependency crossings as well, though having failed to reduce them, to such rarity as found in human languages. These results suggest that chunking may play a vital role in the minimization of dependency distance, and a somewhat contributing role in the rarity of dependency crossing. In addition, the results point to a possibility that the rarity of dependency crossings is not a mere side-effect of minimization of dependency distance, but a linguistic phenomenon with its own motivations. 

• [cs.CV]A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place
Colin Rennie, Rahul Shome, Kostas E. Bekris, Alberto F. De Souza
//arxiv.org/abs/1509.01277v1 

An important logistics application of robotics involves manipulators that pick-and-place objects placed in warehouse shelves. A critical aspect of this task corre- sponds to detecting the pose of a known object in the shelf using visual data. Solving this problem can be assisted by the use of an RGB-D sensor, which also provides depth information beyond visual data. Nevertheless, it remains a challenging problem since multiple issues need to be addressed, such as low illumination inside shelves, clutter, texture-less and reflective objects as well as the limitations of depth sensors. This paper provides a new rich data set for advancing the state-of-the-art in RGBD- based 3D object pose estimation, which is focused on the challenges that arise when solving warehouse pick- and-place tasks. The publicly available data set includes thousands of images and corresponding ground truth data for the objects used during the first Amazon Picking Challenge at different poses and clutter conditions. Each image is accompanied with ground truth information to assist in the evaluation of algorithms for object detection. To show the utility of the data set, a recent algorithm for RGBD-based pose estimation is evaluated in this paper. Based on the measured performance of the algorithm on the data set, various modifications and improvements are applied to increase the accuracy of detection. These steps can be easily applied to a variety of different methodologies for object pose detection and improve performance in the domain of warehouse pick-and-place. 

• [cs.CV]An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes
Sileye Ba, Xavier Alameda-Pineda, Alessio Xompero, Radu Horaud
//arxiv.org/abs/1509.01520v1 

Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thorougly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem. In this paper, we propose an on-line variational Bayesian model for multi-person tracking from cluttered visual observations provided by person detectors. The paper has the following contributions: A Bayesian framework for tracking an unknown and varying number of persons, a variational expectation-maximization (VEM) algorithm with closed-form expressions for the posterior distributions and model parameter estimation, A method capable of exploiting observations from multiple detectors, thus enabling multimodal fusion, and built-in object-birth and object-visibility processes that allow to handle person appearance and disappearance. The method is evaluated on standard datasets, which shows competitive and encouraging results with respect to state of the art methods. 

• [cs.CV]CNN Based Hashing for Image Retrieval
Jinma Guo, Jianmin Li
//arxiv.org/abs/1509.01354v1 

Along with data on the web increasing dramatically, hashing is becoming more and more popular as a method of approximate nearest neighbor search. Previous supervised hashing methods utilized similarity/dissimilarity matrix to get semantic information. But the matrix is not easy to construct for a new dataset. Rather than to reconstruct the matrix, we proposed a straightforward CNN-based hashing method, i.e. binarilizing the activations of a fully connected layer with threshold 0 and taking the binary result as hash codes. This method achieved the best performance on CIFAR-10 and was comparable with the state-of-the-art on MNIST. And our experiments on CIFAR-10 suggested that the signs of activations may carry more information than the relative values of activations between samples, and that the co-adaption between feature extractor and hash functions is important for hashing. 

• [cs.CV]Conjugate Gradient Acceleration of Non-Linear Smoothing Filters
Andrew Knyazev, Alexander Malyshev
//arxiv.org/abs/1509.01514v1 

The most efficient signal edge-preserving smoothing filters, e.g., for denoising, are non-linear. Thus, their acceleration is challenging and is often performed in practice by tuning filter parameters, such as by increasing the width of the local smoothing neighborhood, resulting in more aggressive smoothing of a single sweep at the cost of increased edge blurring. We propose an alternative technology, accelerating the original filters without tuning, by running them through a special conjugate gradient method, not affecting their quality. The filter non-linearity is dealt with by careful freezing and restarting. Our initial numerical experiments on toy one-dimensional signals demonstrate 20x acceleration of the classical bilateral filter and 3-5x acceleration of the recently developed guided filter. 

• [cs.CV]EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Israel D. Gebru, Xavier Alameda-Pineda, Florence Forbes, Radu Horaud
//arxiv.org/abs/1509.01509v1 

Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical properties and to the existence of maximum-likelihood estimators based on expectation-maximization (EM). In this paper we propose a new mixture model that associates a weight with each observed point. We introduce the weighted-data Gaussian mixture and we derive two EM algorithms. The first one considers a fixed weight for each observation. The second one treats each weight as a random variable following a gamma distribution. We propose a model selection method based on a minimum message length criterion, provide a weight initialization strategy, and validate the proposed algorithms by comparing them with several state of the art parametric and non-parametric clustering techniques. We also demonstrate the effectiveness and robustness of the proposed clustering technique in the presence of heterogeneous data, namely audio-visual scene analysis. 

• [cs.CV]Image Classification with Rejection using Contextual Information
Filipe Condessa, José Bioucas-Dias, Carlos Castro, John Ozolek, Jelena Kovačević
//arxiv.org/abs/1509.01287v1 

We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information. Rejection is desired in image-classification applications that require a robust classifier but not the classification of the entire image. The proposed algorithm combines local and multiscale contextual information with rejection, improving the classification performance. As a probabilistic model for classification, we adopt a multinomial logistic regression. The concept of rejection with contextual information is implemented by modeling the classification problem as an energy minimization problem over a graph representing local and multiscale similarities of the image. The rejection is introduced through an energy data term associated with the classification risk and the contextual information through an energy smoothness term associated with the local and multiscale similarities within the image. We illustrate the proposed method on the classification of images of H&E-stained teratoma tissues. 

• [cs.CV]Learning Temporal Alignment Uncertainty for Efficient Event Detection
Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey
//arxiv.org/abs/1509.01343v1 

In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and efficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence. A drawback to the BOW representation, however, is the intrinsic destruction of the temporal ordering information. In this paper we propose a new representation that leverages the uncertainty in relative temporal alignments between pairs of sequences while not destroying temporal ordering. Our representation, like BOW, is of a fixed dimensionality making it easily integrated with a linear detection function. Extensive experiments on CK+, 6DMG, and UvA-NEMO databases show significant performance improvements across both isolated and continuous event detection tasks. 

• [cs.CV]Semantic Amodal Segmentation
Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollár
//arxiv.org/abs/1509.01329v1 

Common visual recognition tasks such as classification, object detection, and semantic segmentation are rapidly reaching maturity, and given the recent rate of progress, it is not unreasonable to conjecture that techniques for many of these problems will approach human levels of performance in the next few years. In this paper we look to the future: what is the next frontier in visual recognition? We offer one possible answer to this question. We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. To date, we have labeled 500 images in the BSDS dataset with at least five annotators per image. Critically, the resulting full scene annotation is surprisingly consistent between annotators. For example, for edge detection our annotations have substantially higher human consistency than the original BSDS edges while providing a greater challenge for existing algorithms. We are currently annotating ~5000 images from the MS COCO dataset. 

• [cs.DC]The Anatomy of Big Data Computing
Raghavendra Kune, Pramodkumar Konugurthi, Arun Agarwal, Raghavendra Rao Chillarige, Rajkumar Buyya
//arxiv.org/abs/1509.01331v1 

Advances in information technology and its widespread growth in several areas of business, engineering, medical and scientific studies are resulting in information/data explosion. Knowledge discovery and decision making from such rapidly growing voluminous data is a challenging task in terms of data organization and processing, which is an emerging trend known as Big Data Computing; a new paradigm which combines large scale compute, new data intensive techniques and mathematical models to build data analytics. Big Data computing demands a huge storage and computing for data curation and processing that could be delivered from on-premise or clouds infrastructures. This paper discusses the evolution of Big Data computing, differences between traditional data warehousing and Big Data, taxonomy of Big Data computing and underpinning technologies, integrated platform of Big Data and Clouds known as Big Data Clouds, layered architecture and components of Big Data Cloud and finally discusses open technical challenges and future directions. 

• [cs.IR]Incremental Active Opinion Learning Over a Stream of Opinionated Documents
Max Zimmermann, Eirini Ntoutsi, Myra Spiliopoulou
//arxiv.org/abs/1509.01288v1 

Applications that learn from opinionated documents, like tweets or product reviews, face two challenges. First, the opinionated documents constitute an evolving stream, where both the author’s attitude and the vocabulary itself may change. Second, labels of documents are scarce and labels of words are unreliable, because the sentiment of a word depends on the (unknown) context in the author’s mind. Most of the research on mining over opinionated streams focuses on the first aspect of the problem, whereas for the second a continuous supply of labels from the stream is assumed. Such an assumption though is utopian as the stream is infinite and the labeling cost is prohibitive. To this end, we investigate the potential of active stream learning algorithms that ask for labels on demand. Our proposed ACOSTREAM 1 approach works with limited labels: it uses an initial seed of labeled documents, occasionally requests additional labels for documents from the human expert and incrementally adapts to the underlying stream while exploiting the available labeled documents. In its core, ACOSTREAM consists of a MNB classifier coupled with “sampling” strategies for requesting class labels for new unlabeled documents. In the experiments, we evaluate the classifier performance over time by varying: (a) the class distribution of the opinionated stream, while assuming that the set of the words in the vocabulary is fixed but their polarities may change with the class distribution; and (b) the number of unknown words arriving at each moment, while the class polarity may also change. Our results show that active learning on a stream of opinionated documents, delivers good performance while requiring a small selection of labels 

• [cs.LG]Deep Broad Learning - Big Models for Big Data
Nayyar A. Zaidi, Geoffrey I. Webb, Mark J. Carman, Francois Petitjean
//arxiv.org/abs/1509.01346v1 

Deep learning has demonstrated the power of detailed modeling of complex high-order (multivariate) interactions in data. For some learning tasks there is power in learning models that are not only Deep but also Broad. By Broad, we mean models that incorporate evidence from large numbers of features. This is of especial value in applications where many different features and combinations of features all carry small amounts of information about the class. The most accurate models will integrate all that information. In this paper, we propose an algorithm for Deep Broad Learning called DBL. The proposed algorithm has a tunable parameter $n$, that specifies the depth of the model. It provides straightforward paths towards out-of-core learning for large data. We demonstrate that DBL learns models from large quantities of data with accuracy that is highly competitive with the state-of-the-art. 

• [cs.LG]Diffusion-KLMS Algorithm and its Performance Analysis for Non-Linear Distributed Networks
Rangeet Mitra, Vimal Bhatia
//arxiv.org/abs/1509.01352v1 

In a distributed network environment, the diffusion-least mean squares (LMS) algorithm gives faster convergence than the original LMS algorithm. It has also been observed that, the diffusion-LMS generally outperforms other distributed LMS algorithms like spatial LMS and incremental LMS. However, both the original LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable. A variant of LMS called kernel-LMS (KLMS) has been proposed in the literature for such non-linearities. In this paper, we propose kernelised version of diffusion-LMS for non-linear distributed environments. Simulations show that the proposed approach has superior convergence as compared to algorithms of the same genre. We also introduce a technique to predict the transient and steady-state behaviour of the proposed algorithm. The techniques proposed in this work (or algorithms of same genre) can be easily extended to distributed parameter estimation applications like cooperative spectrum sensing and massive multiple input multiple output (MIMO) receiver design which are potential components for 5G communication systems. 

• [cs.LG]Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root Growth
Hamidreza Farhidzadeh
//arxiv.org/abs/1509.01270v1 

One of the challenging problems in biology is to classify plants based on their reaction on genetic mutation. Arabidopsis Thaliana is a plant that is so interesting, because its genetic structure has some similarities with that of human beings. Biologists classify the type of this plant to mutated and not mutated (wild) types. Phenotypic analysis of these types is a time-consuming and costly effort by individuals. In this paper, we propose a modified feature extraction step by using velocity and acceleration of root growth. In the second step, for plant classification, we employed different Support Vector Machine (SVM) kernels and two hybrid systems of neural networks. Gated Negative Correlation Learning (GNCL) and Mixture of Negatively Correlated Experts (MNCE) are two ensemble methods based on complementary feature of classical classifiers; Mixture of Expert (ME) and Negative Correlation Learning (NCL). The hybrid systems conserve of advantages and decrease the effects of disadvantages of NCL and ME. Our Experimental shows that MNCE and GNCL improve the efficiency of classical classifiers, however, some SVM kernels function has better performance than classifiers based on neural network ensemble method. Moreover, kernels consume less time to obtain a classification rate. 

• [cs.LG]Parallel and Distributed Approaches for Graph Based Semi-supervised Learning
Konstantin Avrachenkov, Vivek Borkar, Krishnakant Saboo
//arxiv.org/abs/1509.01349v1 

Two approaches for graph based semi-supervised learning are proposed. The firstapproach is based on iteration of an affine map. A key element of the affine map iteration is sparsematrix-vector multiplication, which has several very efficient parallel implementations. The secondapproach belongs to the class of Markov Chain Monte Carlo (MCMC) algorithms. It is based onsampling of nodes by performing a random walk on the graph. The latter approach is distributedby its nature and can be easily implemented on several processors or over the network. Boththeoretical and practical evaluations are provided. It is found that the nodes are classified intotheir class with very small error. The sampling algorithm’s ability to track new incoming nodesand to classify them is also demonstrated. 

• [cs.LG]Probabilistic Neural Network Training for Semi-Supervised Classifiers
Hamidreza Farhidzadeh
//arxiv.org/abs/1509.01271v1 

In this paper, we propose another version of help-training approach by employing a Probabilistic Neural Network (PNN) that improves the performance of the main discriminative classifier in the semi-supervised strategy. We introduce the PNN-training algorithm and use it for training the support vector machine (SVM) with a few numbers of labeled data and a large number of unlabeled data. We try to find the best labels for unlabeled data and then use SVM to enhance the classification rate. We test our method on two famous benchmarks and show the efficiency of our method in comparison with pervious methods. 

• [cs.LG]l1-norm Penalized Orthogonal Forward Regression
Xia Hong, Sheng Chen, Yi Guo, Junbin Gao
//arxiv.org/abs/1509.01323v1 

A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leaveone- out mean square error (LOOMSE). Firstly, a new l1-norm penalized cost function is defined in the constructed orthogonal space, and each orthogonal basis is associated with an individually tunable regularization parameter. Secondly, due to orthogonal computation, the LOOMSE can be analytically computed without actually splitting the data set, and moreover a closed form of the optimal regularization parameter in terms of minimal LOOMSE is derived. Thirdly, a lower bound for regularization parameters is proposed, which can be used for robust LOOMSE estimation by adaptively detecting and removing regressors to an inactive set so that the computational cost of the algorithm is significantly reduced. Illustrative examples are included to demonstrate the effectiveness of this new l1-POFR approach. 

• [cs.NA]Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization
Arnaud Vandaele, Nicolas Gillis, Qi Lei, Kai Zhong, Inderjit Dhillon
//arxiv.org/abs/1509.01404v1 

Given a symmetric nonnegative matrix $A$, symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$, usually with much fewer columns than $A$, such that $A \approx HH^T$. SymNMF can be used for data analysis and in particular for various clustering tasks. In this paper, we propose simple and very efficient coordinate descent schemes to solve this problem, and that can handle large and sparse input matrices. The effectiveness of our methods is illustrated on synthetic and real-world data sets, and we show that they perform favorably compared to recent state-of-the-art methods. 

• [cs.NI]Predicting SLA Violations in Real Time using Online Machine Learning
Jawwad Ahmed, Andreas Johnsson, Rerngvit Yanggratoke, John Ardelius, Christofer Flinta, Rolf Stadler
//arxiv.org/abs/1509.01386v1 

Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SLA violations for user services in telecom environments is difficult, due to time-varying user demands and infrastructure load conditions. In this paper, we propose a service-agnostic online learning approach, whereby the behavior of the system is learned on the fly, in order to predict client-side SLA violations. The approach uses device-level metrics, which are collected in a streaming fashion on the server side. Our results show that the approach can produce highly accurate predictions (>90% classification accuracy and < 10% false alarm rate) in scenarios where SLA violations are predicted for a video-on-demand service under changing load patterns. The paper also highlight the limitations of traditional offline learning methods, which perform significantly worse in many of the considered scenarios. 

• [physics.soc-ph]Ranking nodes in growing networks: When PageRank fails
Manuel Sebastian Mariani, Matus Medo, Yi-Cheng Zhang
//arxiv.org/abs/1509.01476v1 

PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm’s efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank’s performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes. 

• [stat.ME]Latent drop-out transitions in quantile regression
Maria Francesca Marino, Marco Alfó
//arxiv.org/abs/1509.01405v1 

Longitudinal data are characterized by the dependence between observations coming from the same individual. In a regression perspective, such a dependence can be usefully ascribed to unobserved features (covariates) specific to each individual. On these grounds, random parameter models with time-constant or time-varying structure are well established in the generalized linear model context. In the quantile regression framework, specifications based on random parameters have only recently known a flowering interest. We start from the recent proposal by Farcomeni (2012) on longitudinal quantile hidden Markov models, and extend it to handle potentially informative missing data mechanism. In particular, we focus on monotone missingness which may lead to selection bias and, therefore, to unreliable inferences on model parameters. We detail the proposed approach by re-analyzing a well known dataset on the dynamics of CD4 cell counts in HIV seroconverters and by means of a simulation study. 

• [stat.ME]On Combining Estimation Problems Under Quadratic Loss: A Generalization
Severien Nkurunziza
//arxiv.org/abs/1509.01541v1 

The main theorem in Judge and Mittelhammer [Judge, G. G., and Mittelhammer, R. (2004), A Semiparametric Basis for Combining Estimation Problems under Quadratic Loss; JASA, 99, 466, 479–487] stipulates that, in the context of nonzero correlation, a sufficient condition for the Stein rule (SR)-type estimator to dominate the base estimator is that the dimension $k$ should be at least 5. Thanks to some refined inequalities, this dominance result is proved in its full generality; for a class of estimators which includes the SR estimator as a special case. Namely, we prove that, for any member of the derived class, $k\geqslant 3$ is a sufficient condition regardless of the correlation factor. We also relax the Gaussian condition of the distribution of the base estimator, as we consider the family of elliptically contoured variates. Finally, we waive the condition on the invertibility of the variance-covariance matrix of the base and the competing estimators. Our theoretical findings are corroborated by some simulation studies, and the proposed method is applied to the Cigarette dataset. 

• [stat.ME]Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal’s Fail-Safe Number
Konstantinos C. Fragkos, Michail Tsagris, Christos C. Frangos
//arxiv.org/abs/1509.01365v1 

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal’s fail-safe number. Although Rosenthal’s estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal’s fail-safe number.This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal’s estimator. 

• [stat.ML]Minimum Spectral Connectivity Projection Pursuit for Unsupervised Classification
David P. Hofmeyr, Nicos G. Pavlidis, Idris A. Eckley
//arxiv.org/abs/1509.01546v1 

We study the problem of determining the optimal univariate subspace for maximising the separability of a binary partition of unlabeled data, as measured by spectral graph theory. This is achieved by ?nding projections which minimise the second eigenvalue of the Laplacian matrices of the projected data, which corresponds to a non-convex, non-smooth optimisation problem. We show that the optimal projection based on spectral connectivity converges to the vector normal to the maximum margin hyperplane through the data, as the scaling parameter is reduced to zero. This establishes a connection between connectivity as measured by spectral graph theory and maximal Euclidean separation. It also allows us to apply our methodology to the problem of ?nding large margin linear separators. The computational cost associated with each eigen-problem is quadratic in the number of data. To mitigate this problem, we propose an approximation method using microclusters with provable approximation error bounds. We evaluate the performance of the proposed method on simulated and publicly available data sets and ?nd that it compares favourably with existing methods for projection pursuit and dimension reduction for unsupervised data partitioning.