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arXiv 2017年

作者： Pham, Trang Tran, Truyen Dam, Hoa Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia School of Computing and Information Technology University of Wollongong Australia

Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to augment an attributed graph with a virtual node that is bidirectionally connected to all existing nodes. The virtual node represents the latent aspects of the graph, which are not immediately available from the attributes and local connectivity structures. The expanded graph is then put through any node representation method. The representation of the virtual node is then the representation of the entire graph. In this paper, we use the recently introduced Column Network for the expanded graph, resulting in a new end-to-end graph classification model dubbed Virtual Column Network (VCN). The model is validated on two tasks: (i) predicting bioactivity of chemical compounds, and (ii) finding software vulnerability from source code. Results demonstrate that VCN is competitive against wellestablished rivals. Copyright © 2017, The Authors. All rights reserved.

关键词： Graph theory

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Scalable Support Vector Clustering Using Budget

arXiv

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arXiv 2017年

作者： Pham, Tung Le, Trung Dang, Hang Faculty of Information Technology VNUHCM-University of Science Viet Nam Centre for Pattern Recognition and Data Analytics Deakin University Australia

Owing to its application in solving the difficult and diverse clustering or outlier detection problem, support-based clustering has recently drawn plenty of attention. Support-based clustering method always undergoes two phases: finding the domain of novelty and performing clustering assignment. To find the domain of novelty, the training time given by the current solvers is typically over-quadratic in the training size, and hence precluding the usage of support-based clustering method for large-scale datasets. In this paper, we propose applying Stochastic Gradient Descent (SGD) framework to the first phase of support-based clustering for finding the domain of novelty and a new strategy to perform the clustering assignment. However, the direct application of SGD to the first phase of support-based clustering is vulnerable to the curse of kernelization, that is, the model size linearly grows up with the data size accumulated overtime. To address this issue, we invoke the budget approach which allows us to restrict the model size to a small budget. Our new strategy for clustering assignment enables a fast computation by means of reducing the task of clustering assignment on the full training set to the same task on a significantly smaller set. We also provide a rigorous theoretical analysis about the convergence rate for the proposed method. Finally, we validate our proposed method on the well-known datasets for clustering to show that the proposed method offers a comparable clustering quality while simultaneously achieving significant speedup in comparison with the baselines. Copyright © 2017, The Authors. All rights reserved.

关键词： Cluster analysis

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Outlier detection on mixed-type data: An energy-based approach 12th

Outlier detection on mixed-type data: An energy-based approa...

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12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Do, Kien Tran, Truyen Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9783319495859

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (***). The *** is a principled probabilistic method that models data density. We propose to use free-energy derived from *** as outlier score to detect outliers as those data points lying in low density regions. The method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of *** is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts. © Springer International Publishing AG 2016.

关键词： Free energy

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Exceptional contrast set mining: Moving beyond the deluge of the obvious 29th

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29th Australasian Joint Conference on Artificial Intelligence, AI 2016

作者： Nguyen, Dang Luo, Wei Phung, Dinh Venkatesh, Svetha School of Information Technology Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9783319501260

data scientists, with access to fast growing data and computing power, constantly look for algorithms with greater detection power to discover "novel" knowledge. But more often than not, their algorithms give them too many outputs that are either highly speculative or simply confirming what the domain experts already know. To escape this dilemma, we need algorithms that move beyond the obvious association analyses and leverage domain analytic objectives (aka. KPIs) to look for higher order connections. We propose a new technique Exceptional Contrast Set Mining that first gathers a succinct collection of affirmative contrast sets based on the principle of redundant information elimination. Then it discovers exceptional contrast sets that contradict the affirmative contrast sets. The algorithm has been successfully applied to several analytic consulting projects. In particular, during an analysis of a state-wide cancer registry, it discovered a surprising regional difference in breast cancer screening. © Springer International Publishing AG 2016.

关键词： Diseases

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$\mathtt {Deepr}$: A Convolutional Net for Medical Records

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IEEE journal of biomedical and health informatics 2017年第1期21卷 22-30页

作者： Phuoc Nguyen Truyen Tran Nilmini Wickramasinghe Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Faculty of Science and Technology Deakin University Geelong Vic Australia Health Informatics Management Deakin University Geelong Vic Australia Health Information Management Epworth HealthCare Richmond Vic Australia

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.

关键词： Medical diagnostic imaging Machine learning Diseases Feature extraction Hospitals Electronic medical records

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SECC: Simultaneous extraction of context and community from pervasive signals 14

SECC: Simultaneous extraction of context and community from ...

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14th IEEE International Conference on Pervasive Computing and Communications, PerCom 2016

作者： Nguyen, Thuong Nguyen, Vu Salim, Flora D. Phung, Dinh Computer Science and Information Technology RMIT University Australia Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467387798

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture highorder and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated. © 2016 IEEE.

关键词： Ubiquitous computing

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Process-constrained batch Bayesian optimisation 17

Process-constrained batch Bayesian optimisation

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Pratibha Vellanki Santu Rana Sunil Gupta David Rubin Alessandra Sutti Thomas Dorin Murray Height Paul Sandars Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Institute for Frontier Materials GTP Research Deakin University Geelong Australia Materials Science and Engineering Michigan Technological University

ISBN: (纸本)9781510860964

Prevailing batch Bayesian optimisation methods allow all control variables to be freely altered at each iteration. Real-world experiments, however, often have physical limitations making it time-consuming to alter all settings for each recommendation in a batch. This gives rise to a unique problem in BO: in a recommended batch, a set of variables that are expensive to experimentally change need to be fixed, while the remaining control variables can be varied. We formulate this as a process-constrained batch Bayesian optimisation problem. We propose two algorithms, pc-BO(basic) and pc-BO(nested). pc-BO(basic) is simpler but lacks convergence guarantee. In contrast pc-BO(nested) is slightly more complex, but admits convergence analysis. We show that the regret of pc-BO(nested) is sublinear. We demonstrate the performance of both pc-BO(basic) and pc-BO(nested) by optimising benchmark test functions, tuning hyper-parameters of the SVM classifier, optimising the heat-treatment process for an Al-Sc alloy to achieve target hardness, and optimising the short polymer fibre production process.

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The influence of pharmacological autonomic blockades on multi-scale measures of heart rate variability

The influence of pharmacological autonomic blockades on mult...

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Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107

作者： Marzbanrad, Faezeh Karmakar, C.K. Khandoker, A.H. Palaniswami, M. Moritani, T. Jelinek, H.F. Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3168 Australia Centre for Research in Complex Systems and School of Community Health Charles Sturt University Albury Australia Centre of Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Khalifa University of Science Technology and Research Abu Dhabi United Arab Emirates The University of Melbourne ParkvilleVIC3010 Australia Kyoto University Kyoto Japan

ISBN: (纸本)9789811051210

Multi-scale measures have been used extensively to characterize non-linear dynamics of heart beats. However a physiological interpretation of the exponents associated with various scales of these measures and the role of the sympathetic and parasympathetic activities have not been attempted. In this study three multi-scale measures were compared at different scales in a pharmacological autonomic blockade experiment, starting with a control phase. It was then followed by injection of propranolol causing sympathetic blockade and injection of atropine resulting in the blockade of both sympathetic and parasympathetic pathways. Multiscale Entropy (MSE), Multi Fractal Detrended Fluctuation Analysis (MFDFA) and Renyi Entropy (RE) were compared using a paired test statistics. The measures performed differently depending on the scale factors and RE showed the best results indicating a good discrimination particularly at higher scale exponents. This study provides a basis for understanding the effect of autonomic activity on multi-scale measures with respect to changes in parasympathetic and sympathetic activity. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Entropy

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Extracting key challenges in achieving sobriety through shared subspace learning 12th

Extracting key challenges in achieving sobriety through shar...

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12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, Thin Rana, Santu Gupta, Sunil Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Alcohol abuse is quite common among all people without any age restrictions. The uncontrolled use of alcohol affects both the individual and society. Alcohol addiction leads to a huge increase in crime, suicide, health related problems and financial crisis. Research has shown that certain behavioral changes can be effective towards staying abstained. The analysis of behavioral changes of quitters and those who are at the beginning phase of quitting can be useful for reducing the issues related to alcohol addiction. Most of the conventional approaches are based on surveys and, therefore, expensive in both time and cost. Social media has lend itself as a source of large, diverse and unbiased data for analyzing social behaviors. Reddit is a social media platform where a large number of people communicate with each other. It has many different sub-groups called subreddits categorized based on the subject. We collected more than 40,000 self reported user’s data from a subreddit called ‘/r/stopdrinking’. We divide the data into two groups, short-term with abstinent days less than 30 and long-term abstainers with abstinent days greater than 365 based on badge days at the time of post submission. Common and discriminative topics are extracted from the data using JS-NMF, a shared subspace non-negative matrix factorization method. The validity of the extracted topics are demonstrated through predictive performance. © Springer International Publishing AG 2016.

关键词： Non-negative matrix factorization

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Understanding behavioral differences between short and long-term drinking abstainers from social media 12th

Understanding behavioral differences between short and long-...

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12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, Thin Gupta, Sunil Rana, Santu Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Drinking alcohol has high cost on society. The journey from being a regular drinker to a successful quitter may be a long and hard journey, fraught with the risk to relapse. Research has shown that certain behavioral changes can be effective towards staying abstained. Traditional way to conduct research on drinking abstainers uses questionnaire based approach to collect data from a curated group of people. However, it is an expensive approach in both cost and time and often results in small data with less diversity. Recently, social media has emerged as a rich data source. Reddit is one such social media platform that has a community (‘subreddit’) with an interest to quit drinking. The discussions among the group dates back to year 2011 and contain more than 40,000 posts. This large scale data is generated by users themselves and without being limited by any survey questionnaires. The most predictive factors from the features (unigrams, topics and LIWC) associated with short-term and long-term abstinence are identified using Lasso. It is seen that many common patterns manifest in unigrams, topics and LIWC. Whilst topics provided much richer associations between a group of words and the outcome, unigrams and LIWC are found to be good at finding highly predictive solo and psycho linguistically important words. Combining them we have found that many interesting patterns that are associated with the successful attempt made by the long-term abstainer, at the same time finding many of the common issues faced during the initial period of abstinence. © Springer International Publishing AG 2016.

关键词： Surveys

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