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arXiv 2017年

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

The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodologies to effectively handle mixed data by making the data less heterogeneous with minimum loss of information. The other challenge is that such methods must be able to apply in large-scale tasks when dealing with huge amount of mixed data. To tackle these challenges, we introduce parameter sharing and balancing extensions to our recent model, the mixed-variate restricted Boltzmann machine (***) which can transform heterogeneous data into homogeneous representation. We also integrate structured sparsity and distance metric learning into RBM-based models. Our proposed methods are applied in various applications including latent patient profile modelling in medical data analysis and representation learning for image retrieval. The experimental results demonstrate the models perform better than baseline methods in medical data and outperform state-of-the-art rivals in image dataset. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Tensor-variate restricted boltzmann machines 29

Tensor-variate restricted boltzmann machines

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29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015

ISBN: (纸本)9781577357025

Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance. Copyright © 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Tensors

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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

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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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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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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Towards a Video Browser for the Digital Native

Towards a Video Browser for the Digital Native

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IEEE International Conference on multimedia and Expo Workshops (ICMEW)

作者： Brett Adams Stewart Greenhill Svetha Venkatesh Institute for Multi-sensor Processing and Content Analysis (IMPCA) Curtin University of Technology Perth WA Australia Centre for Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong VIC Australia

Almost every aspect of how we create, transmit, and consume video has changed, but video interfaces still mimic those from video's inception. We extend Temporal Semantic Compression for interactive video browsing, which uses an arbitrary frame-by-frame interest measure to sub-sample video in real time, with user interface elements that visualize these measures and the effect of compressing on them. We experiment with a novel interest measure for popularity, and design novel visualizations for expressing interest measures and the compression interaction. We conduct the first formative evaluation of the TSC paradigm, with 8 subjects, and report design implications arising from it.

关键词： Browsers Visualization Motion pictures Color Streaming media Media Semantics

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Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Supervised subspace learning with multi-class Lagrangian SVM...

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24th Australasian Joint Conference on Artificial Intelligence, AI 2011

作者： Pham, Duc-Son Venkatesh, Svetha Institute for Multi-sensor Processing and Content Analysis Curtin University Perth WA Australia

ISBN: (纸本)9783642258312

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation. © 2011 Springer-Verlag.

关键词： Iterative methods

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A context-sensitive device to help people with autism cope with anxiety

A context-sensitive device to help people with autism cope w...

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作者： Mohammedali, Marziya Adams, Brett Phung, Dinh Venkatesh, Svetha Institute for Multi-Sensor Processing and Content Analysis Curtin University Kent St. Bentley WA Australia

ISBN: (纸本)9781450302289

We describe a smartphone application that helps people with Autism Spectrum Disorder (ASD) cope with anxiety attacks. Our prototype provides a one-touch interface for indicating a panic level. The device's response-to instruct, soothe, and/or contact carers-is sensitive to the user's context, consisting of time, location, ambient noise, and nearby friends. Formative evaluation unearths a critical challenge to building assistive technologies for ASD sufferers: can regimented interfaces foster flexible behaviour? Our observations suggest that a delicate balance of design goals is required for a viable assistive technology.

关键词： Assistive technology

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A Visual Data analysis Tool for Sport Player Performance Benchmarking, Comparison, and Change Detection

A Visual Data Analysis Tool for Sport Player Performance Ben...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Sebastian Luhr Mihai Lazarescu Institute for Multi-Sensor Processing and Content Analysis Department of Computing Curtin University of Technology Australia

Sports coaches today have access to a wide variety of information sources that describe the performance of their players. However, despite this great wealth of information, most techniques used to analyse performance require a significant amount of manual processing and continue to rely heavily on input from human experts. In this paper we propose an automated approach to analyse player performance. Specifically, we propose a team benchmarking and concept drift tracking based system that (1) generates adaptive baseline player performance norms, (2) interprets player performance over different time lines and (3) identifies and describes key turning points in player performance. The concept drift technique that we describe uses a combination of overlapping data windows and decision tree based learning to process the data.

关键词： Data analysis Performance analysis Information analysis Data processing Statistical analysis Game theory Australia Video recording Data mining Rhythm

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