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Extraction of Latent patterns and Contexts from Social Honest Signals Using Hierarchical Dirichlet Processes

Extraction of Latent Patterns and Contexts from Social Hones...

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IEEE International Conference on Pervasive Computing and Communications

作者： Thuong Nguyen Dinh Phung Sunil Gupta S. Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University

ISBN: (纸本)9781467345736

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

关键词： Sensor data Ubiquitous computing HIGH DELTA PRESSURE Frameworks AUTOMATIC

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Learning parts-based representations with nonnegative restricted boltzmann machine 5

Learning parts-based representations with nonnegative restri...

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5th Asian Conference on Machine Learning, ACML 2013

作者： Nguyeny, Tu Dinh Tranyz, Truyen Phungy, Dinh Venkateshy, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia Institute for Multi-Sensor Processing and Content Analysis Curtin University Australia

The success of any machine learning system depends critically on effective representations of data. In many cases, especially those in vision, it is desirable that a representation scheme uncovers the parts-based, additive nature of the data. Of current representation learning schemes, restricted Boltzmann machines (RBMs) have proved to be highly effective in unsupervised settings. However, when it comes to parts-based discovery, RBMs do not usually produce satisfactory results. We enhance such capacity of RBMs by introducing nonnegativity into the model weights, resulting in a variant called nonnegative restricted Boltzmann machine (NRBM). The NRBM produces not only controllable decomposition of data into interpretable parts but also offers a way to estimate the intrinsic nonlinear dimensionality of data. We demonstrate the capacity of our model on well-known datasets of handwritten digits, faces and documents. The decomposition quality on images is comparable with or better than what produced by the nonnegative matrix factorisation (NMF), and the thematic features uncovered from text are qualitatively interpretable in a similar manner to that of the latent Dirichlet allocation (LDA). However, the learnt features, when used for classification, are more discriminative than those discovered by both NMF and LDA and comparable with those by RBM. © 2013 T.D. Nguyen, T. Tran, D. Phung & S. Venkatesh.

关键词： Semantics

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Learning sparse latent representation and distance metric for image retrieval

Learning sparse latent representation and distance metric fo...

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2013 IEEE International Conference on Multimedia and Expo, ICME 2013

作者： Nguyen, Tu Dinh Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia Institute for Multi-Sensor Processing and Content Analysis Curtin University Australia

ISBN: (纸本)9781479900152

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG. © 2013 IEEE.

关键词： Image retrieval

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Discriminative k-Means Clustering

Discriminative k-Means Clustering

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International Joint Conference on Neural Networks

作者： Ognjen Arandjelovi? Centre for Pattern Recognition and Data Analytics at Deakin University.

ISBN: (纸本)9781467361279

The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of "positively" and "negatively" labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.

关键词： variety problems k-means

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Interactive browsing system for anomaly video surveillance

Interactive browsing system for anomaly video surveillance

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Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP)

作者： Tien-Vu Nguyen Dinh Phung Sunil Gupta Svetha Venkatesh Centre for Pattern Recognition and Data Analytics (PRaDA) Deakin University Australia

Existing anomaly detection methods in video surveillance exhibit lack of congruence between rare events detected by algorithms and what is considered anomalous by users. This paper introduces a novel browsing model to address this issue, allowing users to interactively examine rare events in an intuitive manner. Introducing a novel way to compute rare motion patterns, we estimate latent factors of foreground motion patterns through Bayesian Nonparametric Factor analysis. Each factor corresponds to a typical motion pattern. A rarity score for each factor is computed, and ordered in decreasing order of rarity, permitting users to browse events using any proportion of rare factors. Rare events correspond to frames that contain the rare factors chosen. We present the user with an interface to inspect events that incorporate these rarest factors in a spatial-temporal manner. We demonstrate the system on a public video data set, showing key aspects of the browsing paradigm.

关键词： Robustness Principal component analysis Feature extraction Matrix decomposition Cameras data models Hidden Markov models

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Making the most of the self-quotient image in face recognition

Making the most of the self-quotient image in face recogniti...

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International Conference on Automatic Face and Gesture recognition

作者： Ognjen Arandjelović Centre for Pattern Recognition and Data Analytics Deakin University Geelong VIC Australia

ISBN: (纸本)9781467355452

The self-quotient image is a biologically inspired representation which has been proposed as an illumination invariant feature for automatic face recognition. Owing to the lack of strong domain specific assumptions underlying this representation, it can be readily extracted from raw images irrespective of the persons's pose, facial expression etc. What makes the self-quotient image additionally attractive is that it can be computed quickly and in a closed form using simple low-level image operations. However, it is generally accepted that the self-quotient is insufficiently robust to large illumination changes which is why it is mainly used in applications in which low precision is an acceptable compromise for high recall (e.g. retrieval systems). Yet, in this paper we demonstrate that the performance of this representation in challenging illuminations has been greatly underestimated. We show that its error rate can be reduced by over an order of magnitude, without any changes to the representation itself. Rather, we focus on the manner in which the dissimilarity between two self-quotient images is computed. By modelling the dominant sources of noise affecting the representation, we propose and evaluate a series of different dissimilarity measures, the best of which reduces the initial error rate of 63.0% down to only 5.7% on the notoriously challenging YaleB data set.

关键词： Noise Lighting Face Face recognition Robustness Quantization (signal) Error analysis

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Exploiting side information in distance dependent Chinese restaurant processes for data clustering

Exploiting side information in distance dependent Chinese re...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Cheng Li Dinh Phung Santu Rana Svetha Venkatesh Centre for Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong Australia

ISBN: (纸本)9781479900145

Multimedia contents often possess weakly annotated data such as tags, links and interactions. The weakly annotated data is called side information. It is the auxiliary information of data and provides hints for exploring the link structure of data. Most clustering algorithms utilize pure data for clustering. A model that combines pure data and side information, such as images and tags, documents and keywords, can perform better at understanding the underlying structure of data. We demonstrate how to incorporate different types of side information into a recently proposed Bayesian nonparametric model, the distance dependent Chinese restaurant process (DD-CRP). Our algorithm embeds the affinity of this information into the decay function of the DD-CRP when side information is in the form of subsets of discrete labels. It is flexible to measure distance based on arbitrary side information instead of only the spatial layout or time stamp of observations. At the same time, for noisy and incomplete side information, we set the decay function so that the DD-CRP reduces to the traditional Chinese restaurant process, thus not inducing side effects of noisy and incomplete side information. Experimental evaluations on two real-world datasets NUS WIDE and 20 Newsgroups show exploiting side information in DD-CRP significantly improves the clustering performance.

关键词： Clustering algorithms Noise measurement Mutual information Bayes methods Multimedia communication Time measurement Layout

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An integrated framework for suicide risk prediction 13

An integrated framework for suicide risk prediction

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19th ACM SIGKDD International Conference on Knowledge Discovery and data Mining, KDD 2013

作者： Trany, Truyen Phung, Dinh Luo, Wei Harvey, Richard Berk, Michael Venkatesh, Svetha Pattern Recognition and Data Analytics Deakin University Australia Department of Computing Curtin University Australia Mental Health Drugs and Alcohol Barwon HealthVIC Australia School of Medicine Deakin University Australia

ISBN: (纸本)9781450321747

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient's clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in 1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners. Copyright © 2013 ACM.

关键词： Filter banks

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Clustering patient medical records via sparse subspace representation

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17th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2013

作者： Saha, Budhaditya Pham, Duc-Son Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong VIC Australia Institute for Multi-sensor Processing and Content Analysis Department of Computing Curtin University WA Australia

ISBN: (纸本)9783642374555

The health industry is facing increasing challenge with "big data" as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of subspace clustering. Our novel contribution lies in the exploitation of sparse representation to discover subspaces automatically and a domain-specific construction of weighting matrices for patient records. We show the new formulation is readily solved by extending existing 1 -regularized optimization algorithms. Using a cohort of both diabetes and stroke data we show that we outperform existing benchmark clustering techniques in the literature. © Springer-Verlag 2013.

关键词： Clustering algorithms

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Learning sparse latent representation and distance metric for image retrieval

Learning sparse latent representation and distance metric fo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Tu Dinh Nguyen Truyen Tran Dinh Phung Svetha Venkatesh Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia Deakin University Burwood VIC AU

ISBN: (纸本)9781479900145

关键词： Australia Abstracts Whales Rabbits

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