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International Conference on pattern recognition

作者： Cheng Li Santu Rana Dinh Phung Svetha Venkatesh Center for Pattern Recognition and Data Analytics Deakin University

We propose a novel hierarchical Bayesian framework, word-distance-dependent Chinese restaurant franchise (wd-dCRF) for topic discovery from a document corpus regularized by side information in the form of word-to-word relations, with an application on Electronic Medical Records (EMRs). Typically, a EMRs dataset consists of several patients (documents) and each patient contains many diagnosis codes (words). We exploit the side information available in the form of a semantic tree structure among the diagnosis codes for semantically-coherent disease topic discovery. We introduce novel functions to compute word-to-word distances when side information is available in the form of tree structures. We derive an efficient inference method for the wddCRF using MCMC technique. We evaluate on a real world medical dataset consisting of about 1000 patients with PolyVascular disease. Compared with the popular topic analysis tool, hierarchical Dirichlet process (HDP), our model discovers topics which are superior in terms of both qualitative and quantitative measures.

关键词： Diseases Bayes methods Medical diagnostic imaging Indexes Hospitals Correlation Predictive models

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Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach 12

Large-scale statistical modeling of motion patterns: A Bayes...

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8th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2012

作者： Rana, Santu Phung, Dinh Pham, Sonny Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Waurn Ponds VIC 3216 Australia Department of Computing Curtin University Bentley WA 6102 Australia

ISBN: (纸本)9781450316606

We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection. © 2012 ACM.

关键词： Bayesian networks

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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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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

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)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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One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems

One-Pass Logistic Regression for Label-Drift and Large-Scale...

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IEEE International Conference on data Mining (ICDM)

作者： Vu Nguyen Tu Dinh Nguyen Trung Le Svetha Venkatesh Dinh Phung Center for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509054749

Logistic regression (LR) for classification is the workhorse in industry, where a set of predefined classes is required. The model, however, fails to work in the case where the class labels are not known in advance, a problem we term label-drift classification. Label-drift classification problem naturally occurs in many applications, especially in the context of streaming settings where the incoming data may contain samples categorized with new classes that have not been previously seen. Additionally, in the wave of big data, traditional LR methods may fail due to their expense of running time. In this paper, we introduce a novel variant of LR, namely one-pass logistic regression (OLR) to offer a principled treatment for label-drift and large-scale classifications. To handle largescale classification for big data, we further extend our OLR to a distributed setting for parallelization, termed sparkling OLR (Spark-OLR). We demonstrate the scalability of our proposed methods on large-scale datasets with more than one hundred million data points. The experimental results show that the predictive performances of our methods are comparable orbetter than those of state-of-the-art baselines whilst the executiontime is much faster at an order of magnitude. In addition, the OLR and Spark-OLR are invariant to data shuffling and have no hyperparameter to tune that significantly benefits data practitioners and overcomes the curse of big data cross-validationto select optimal hyperparameters.

关键词： Logistics Bayes methods Estimation Big data data models Industries Context

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Forecasting Patient Outflow from Wards having No Real-Time Clinical data

Forecasting Patient Outflow from Wards having No Real-Time C...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Shivapratap Gopakumar Truyen Tran Wei Luo Dinh Phung Svetha Venkatesh Center for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509061181

Modelling patient flow is crucial in understanding resource demand and prioritization. To date, there has been limited work in predicting ward-level discharges. Our study investigates forecasting total next-day discharges from an open ward. In the absence of real-time clinical data, we propose to construct a feature set from patient demographics, ward data and discharge time series to derive a random forest model for forecasting daily discharge. Using data from a general ward of a large regional Australian hospital, we compared our random forest model with a classical auto-regressive integrated moving average (ARIMA) for 12,141 patient visits over 1826 days. Forecasting quality was measured using Mean Forecast Error, Mean Absolute Error, symmetric Mean Absolute Percentage Error and Root Mean Square Error. When compared to the baseline model, next day discharge forecasts using random forests achieved 17.4 % improvement in Mean Absolute Error, for all days in the year 2014.

关键词： Forecasting Time series analysis Predictive models Hospitals data models Autoregressive processes Vegetation

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Nonnegative restricted boltzmann machines for parts-based representations discovery and predictive model stabilization

arXiv

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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 Deakin University Australia

The success of any machine learning system depends critically on effective representations of data. In many cases, 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 *** 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, and helps to stabilize linear predictive models. We demonstrate the capacity of our model on applications such as handwritten digit recognition, face recognition, document classification and patient readmission prognosis. The decomposition quality on images is comparable with or better than what produced by the nonnegative matrix factorization (NMF), and the thematic features uncovered from text are qualitatively interpretable in a similar manner to that of the latent Dirichlet allocation (LDA). The stability performance of feature selection on medical data is better than RBM and competitive with NMF. The learned features, when used for classification, are more discriminative than those discovered by both NMF and LDA and comparable with those by RBM. Copyright © 2017, The Authors. All rights reserved.

关键词： Matrix algebra

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Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Improved risk predictions via sparse imputation of patient c...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Budhaditya Saha Sunil Gupta Svetha Venkatesh Center for Pattern Recognition and Data Analytics (PRaDA) Deakin University Australia

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.

关键词： Sparse matrices Hospitals Cancer Diseases Approximation methods Matrix decomposition Predictive models

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Faster training of very deep networks via p-norm gates

Faster training of very deep networks via p-norm gates

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International Conference on pattern recognition

作者： Trang Pham Truyen Tran Dinh Phung Svetha Venkatesh Center for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9781509048489

A major contributing factor to the recent advances in deep neural networks is structural units that let sensory information and gradients to propagate easily. Gating is one such structure that acts as a flow control. Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforward models such as Residual Nets and Highway Networks. This enables learning in very deep networks with hundred layers and helps achieve record-breaking results in vision (e.g., ImageNet with Residual Nets) and NLP (e.g., machine translation with GRU). However, there is limited work in analysing the role of gating in the learning process. In this paper, we propose a flexible p-norm gating scheme, which allows user-controllable flow and as a consequence, improve the learning speed. This scheme subsumes other existing gating schemes, including those in GRU, Highway Networks and Residual Nets as special cases. Experiments on large sequence and vector datasets demonstrate that the proposed gating scheme helps improve the learning speed significantly without extra overhead.

关键词： Logic gates Road transportation Feedforward neural networks Training Computer architecture Standards

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