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International Conference on pattern recognition

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

ISBN: (纸本)9781479952106

Monitoring daily physical activity of human plays an important role in preventing diseases as well as improving health. In this paper, we demonstrate a framework for monitoring the physical activity levels in daily life. We collect the data using accelerometer sensors in a realistic setting without any supervision. The ground truth of activities is provided by the participants themselves using an experience sampling application running on mobile phones. The original data is discretized by the hierarchical Dirichlet process (HDP) into different activity levels and the number of levels is inferred automatically. We validate the accuracy of the extracted patterns by using them for the multi-label classification of activities and demonstrate the high performances in various standard evaluation metrics. We further show that the extracted patterns are highly correlated to the daily routine of users.

关键词： Accelerometers Feature extraction Sensors Accuracy data models Legged locomotion

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Stable clinical prediction using graph support vector machines

Stable clinical prediction using graph support vector machin...

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International Conference on pattern recognition

作者： Iman Kamkar Sunil Gupta Cheng Li Dinh Phung Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509048489

The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard similarity among features. Our method employs a convex function to penalize the pairwise l ∞ -norm of connected feature coefficients in the graph. We apply the alternating direction method of multipliers to solve the proposed formulation. Our experiments are conducted on a synthetic and three real-world hospital datasets. We show that our proposed method is more stable than the state-of-the-art feature selection and classification techniques in terms of three stability measures namely, Jaccard similarity measure, Spearman's rank correlation coefficient and Kuncheva index. We further show that our method has resulted in better classification performance compared to the baselines.

关键词： Support vector machines Stability criteria Mathematical model Correlation Optimization Indexes

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Dual discriminator generative adversarial nets

arXiv

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arXiv 2017年

作者： Nguyen, Tu Dinh Le, Trung Vu, Hung Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University Australia

We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN). Our idea is intuitive but proven to be very effective, especially in addressing some key limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and reverse KL divergences into a unified objective function, thus it exploits the complementary statistical properties from these divergences to effectively diversify the estimated density in capturing multi-modes. We term our method dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has two discriminators;and together with a generator, it also has the analogy of a minimax game, wherein a discriminator rewards high scores for samples from data distribution whilst another discriminator, conversely, favoring data from the generator, and the generator produces data to fool both two discriminators. We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem. We conduct extensive experiments on synthetic and real-world large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made our best effort to compare our D2GAN with the latest state-of-The-Art GAN's variants in comprehensive qualitative and quantitative evaluations. The experimental results demonstrate the competitive and superior performance of our approach in generating good quality and diverse samples over baselines, and the capability of our method to scale up to ImageNet database. Copyright © 2017, The Authors. All rights reserved.

关键词： Discriminators

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Effect of social capital on emotion, language style and latent topics in online depression community

Effect of social capital on emotion, language style and late...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Bo Dao Thin Nguyen Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509041350

Social capital is linked to mental illness. It has been proposed that higher social capital is associated with better mental well-being in both individuals and groups in offline setting. However, in online settings, the association between online social capital and mental health conditions has not yet been explored. Social media offer us a rich opportunity to determine the link between social capital and aspects of mental wellbeing. In this paper, we examine social capital based on levels of social connectivity of bloggers can be connected to aspects of depression in individuals and online depression community. We explore apparent properties of textual contents, including expressed emotions, language styles and latent topics, of a large corpus of blog posts, to analyze the aspect of social capital in the community. Using data collected from online Livejoumal depression community, we apply both statistical tests and machine learning approaches to examine how predictive factors vary between low and high social capital groups. Significant differences are found between low and high social capital groups when characterized by a set of latent topics, language features derived from blog posts, suggesting discriminative features, proved to be useful in the classification task. This shows that linguistic styles are better predictors than latent topics as features. The findings indicate the potential of using social media as a sensor for monitoring mental well-being in online settings.

关键词： Blogs Feature extraction Pragmatics Social network services Adaptation models Monitoring Sociology

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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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Hyperparameter tuning for big data using Bayesian optimisation

Hyperparameter tuning for big data using Bayesian optimisati...

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International Conference on pattern recognition

作者： Tinu Theckel Joy Santu Rana Sunil Gupta Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509048489

Hyperparameters play a crucial role in the model selection of machine learning algorithms. Tuning these hyperparameters can be exhaustive when the data is large. Bayesian optimisation has emerged as an efficient tool for hyperparameter tuning of machine learning algorithms. In this paper, we propose a novel framework for tuning the hyperparameters for big data using Bayesian optimisation. We divide the big data into chunks and generate hyperparameter configurations for the chunks using the standard Bayesian optimisation. We utilise this information from the chunks for hyperparameter tuning on big data using a transfer learning setting. We evaluate the performance of the proposed method on the task of tuning hyperparameters of two machine learning algorithms. We show that our method achieves the best available hyperparameter configuration within less computational time compared to the state-of-art hyperparameter tuning methods.

关键词： Big data Bayes methods Optimization Tuning data models Gaussian processes Noise measurement

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Nonparametric Discovery of Learning patterns and Autism Subgroups from Therapeutic data

Nonparametric Discovery of Learning Patterns and Autism Subg...

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International Conference on pattern recognition

作者： Pratibha Vellanki Thi Duong Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University {pvellank

ISBN: (纸本)9781479952106

Autism Spectrum Disorder (ASD) is growing at a staggering rate, but, little is known about the cause of this condition. Inferring learning patterns from therapeutic performance data, and subsequently clustering ASD children into subgroups, is important to understand this domain, and more importantly to inform evidence-based intervention. However, this data-driven task was difficult in the past due to insufficiency of data to perform reliable analysis. For the first time, using data from a recent application for early intervention in autism (TOBY Play pad), whose download count is now exceeding 4500, we present in this paper the automatic discovery of learning patterns across 32 skills in sensory, imitation and language. We use unsupervised learning methods for this task, but a notorious problem with existing methods is the correct specification of number of patterns in advance, which in our case is even more difficult due to complexity of the data. To this end, we appeal to recent Bayesian nonparametric methods, in particular the use of Bayesian Nonparametric Factor Analysis. This model uses Indian Buffet Process (IBP) as prior on a binary matrix of infinite columns to allocate groups of intervention skills to children. The optimal number of learning patterns as well as subgroup assignments are inferred automatically from data. Our experimental results follow an exploratory approach, present different newly discovered learning patterns. To provide quantitative results, we also report the clustering evaluation against K-means and Nonnegative matrix factorization (NMF). In addition to the novelty of this new problem, we were able to demonstrate the suitability of Bayesian nonparametric models over parametric rivals.

关键词： Variable speed drives Entropy Bayes methods Autism data analysis Education Computational modeling

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Nonparametric discovery of online mental health-related communities

Nonparametric discovery of online mental health-related comm...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Bo Dao Thin Nguyen Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Australia

People are increasingly using social media, especially online communities, to discuss mental health issues and seek supports. Understanding topics, interaction, sentiment and clustering structures of these communities informs important aspects of mental health. It can potentially add knowledge to the underlying cognitive dynamics, mood swings patterns, shared interests, and interaction. There has been growing research interest in analyzing online mental health communities; however sentiment analysis of these communities has been largely under-explored. This study presents an analysis of online Live Journal communities with and without mental health-related conditions including depression and autism. Latent topics for mood tags, affective words, and generic words in the content of the posts made in these communities were learned using nonparametric topic modelling. These representations were then input into a nonparametric clustering to discover meta-groups among the communities. The best performance results can be achieved on clustering communities with latent mood-based representation for such communities. The study also found significant differences in usage latent topics for mood tags and affective features between online communities with and without affective disorders. The findings reveal useful insights into hyper-group detection of online mental health-related communities.

关键词： Media Mood Blogs Autism Sentiment analysis Variable speed drives

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Differentially Private Random Forest with High Utility

Differentially Private Random Forest with High Utility

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IEEE International Conference on data Mining (ICDM)

作者： Santu Rana Sunil Kumar Gupta Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467395052

Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We propose a novel random forest algorithm under the framework of differential privacy. Unlike previous works that strictly follow differential privacy and keep the complete data distribution approximately invariant to change in one data instance, we only keep the necessary statistics (e.g. variance of the estimate) invariant. This relaxation results in significantly higher utility. To realize our approach, we propose a novel differentially private decision tree induction algorithm and use them to create an ensemble of decision trees. We also propose feasible adversary models to infer about the attribute and class label of unknown data in presence of the knowledge of all other data. Under these adversary models, we derive bounds on the maximum number of trees that are allowed in the ensemble while maintaining privacy. We focus on binary classification problem and demonstrate our approach on four real-world datasets. Compared to the existing privacy preserving approaches we achieve significantly higher utility.

关键词： Decision trees Privacy data privacy Vegetation Sensitivity Standards

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A nonparametric Bayesian Poisson gamma model for count data

A nonparametric Bayesian Poisson gamma model for count data

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International Conference on pattern recognition

作者： Sunil Kumar Gupta Dinh Phung Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467322164

We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models.

关键词： data models Analytical models Bayesian methods Dictionaries Face Load modeling Indexes

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