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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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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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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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Dual Space Gradient Descent for Online Learning 16

Dual Space Gradient Descent for Online Learning

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Annual Conference on Neural Information Processing Systems

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

ISBN: (纸本)9781510838819

One crucial goal in kernel online learning is to bound the model size. Common approaches employ budget maintenance procedures to restrict the model sizes using removal, projection, or merging strategies. Although projection and merging, in the literature, are known to be the most effective strategies, they demand extensive computation whilst removal strategy fails to retain information of the removed vectors. An alternative way to address the model size problem is to apply random features to approximate the kernel function. This allows the model to be maintained directly in the random feature space, hence effectively resolve the curse of kernelization. However, this approach still suffers from a serious shortcoming as it needs to use a high dimensional random feature space to achieve a sufficiently accurate kernel approximation. Consequently, it leads to a significant increase in the computational cost. To address all of these aforementioned challenges, we present in this paper the Dual Space Gradient Descent (DualSGD), a novel framework that utilizes random features as an auxiliary space to maintain information from data points removed during budget maintenance. Consequently, our approach permits the budget to be maintained in a simple, direct and elegant way while simultaneously mitigating the impact of the dimensionality issue on learning performance. We further provide convergence analysis and extensively conduct experiments on five real-world datasets to demonstrate the predictive performance and scalability of our proposed method in comparison with the state-of-the-art baselines.

关键词： model size e-learning Eigenvector Dual space Budgets merger descent Seeds

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Approximation vector machines for large-scale online learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Trung Le Tu Dinh Nguyen Vu Nguyen Dinh Phung Centre for Pattern Recognition and Data Analytics School of Information Technology Deakin University Australia

One of the most challenging problems in kernel online learning is to bound the model size and to promote model sparsity. Sparse models not only improve computation and memory usage, but also enhance the generalization capacity - a principle that concurs with the law of parsimony. However, inappropriate sparsity modeling may also significantly degrade the performance. In this paper, we propose Approximation Vector Machine (AVM), a model that can simultaneously encourage sparsity and safeguard its risk in compromising the performance. In an online setting context, when an incoming instance arrives, we approximate this instance by one of its neighbors whose distance to it is less than a predefined threshold. Our key intuition is that since the newly seen instance is expressed by its nearby neighbor the optimal performance can be analytically formulated and maintained. We develop theoretical foundations to support this intuition and further establish an analysis for the common loss functions including Hinge, smooth Hinge, and Logistic (i.e., for the classification task) and l1, l2, and ε-insensitive (i.e., for the regression task) to characterize the gap between the approximation and optimal solutions. This gap crucially depends on two key factors including the frequency of approximation (i.e., how frequent the approximation operation takes place) and the predefined threshold. We conducted extensive experiments for classification and regression tasks in batch and online modes using several benchmark datasets. The quantitative results show that our proposed AVM obtained comparable predictive performances with current state-of-the-art methods while simultaneously achieving significant computational speed-up due to the ability of the proposed AVM in maintaining the model size.

关键词： big data convergence analysis core set kernel large-scale machine learning online learning sparsity stochastic gradient descent

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SECC: Simultaneous extraction of context and community from pervasive signals

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

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

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

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.

关键词： Context data mining Feature extraction Context modeling data models Mixture models Bluetooth

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Learning Multifaceted Latent Activities from Heterogeneous Mobile data

Learning Multifaceted Latent Activities from Heterogeneous M...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Thanh-Binh Nguyen Vu Nguyen Thuong Nguyen Svetha Venkatesh Mohan Kumar Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Australia The Australian e-Health Research Centre CSIRO. Herston Queensland Australia

ISBN: (纸本)9781509052073

Inferring abstract contexts and activities from heterogeneous data is vital to context-aware ubiquitous applications but still remains one of the most challenging problems. Recent advances in Bayesian nonparametric machine learning, in particular the theory of topic models based on Hierarchical Dirichlet Process (HDP), has provided an elegant solution towards these challenges. However, limited existing methods have addressed the problem of inferring latent multifaceted activities and contexts from heterogeneous data sources such as those collected from mobile devices. In this paper, we extend the original HDP to model heterogeneous data using a richer structure of the base measure being a product-space. The proposed model, called product-space HDP (PS-HDP), naturally handles the heterogeneous data from multiple sources and identify the unknown number of latent structures in a principle way. Although this framework is generic, our current work primarily focuses on inferring (latent) threefold activities of who-when-where simultaneously, which corresponds to inducing activities from data collected for identity, location and time. We demonstrate our model on synthetic data as well as on a real-world dataset - the StudentLife dataset. We report results and provide analysis on the discovered activities and patterns to demonstrate the merit of the model. We also quantitatively evaluate the performance of PS-HDP model using standard metrics including F1-score, NMI, RI, purity, and compare them with well-known existing baseline methods.

关键词： data models Context Bluetooth IEEE 802.11 Standard Bayes methods Computational modeling Mobile communication

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Streaming variational inference for dirichlet process mixtures 7

Streaming variational inference for dirichlet process mixtur...

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7th Asian Conference on Machine Learning, ACML 2015

作者： Huynh, Viet Phung, Dinh Venkatesh, Svetha Pattern Recognition and Data Analytics Centre Deakin University Australia

Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they require truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our experiments demonstrate the usefulness of our framework in both synthetic and real-world data. © 2015 V. Huynh, D. Phung & S. Venkatesh.

关键词： Mixtures

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Nonparametric discovery of online mental health-related communities

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

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IEEE International Conference on data Science and Advanced analytics, DSAA 2015

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

ISBN: (纸本)9781467382731

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. © 2015 IEEE.

关键词： Sentiment analysis

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Exploiting feature relationships towards stable feature selection

Exploiting feature relationships towards stable feature sele...

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IEEE International Conference on data Science and Advanced analytics, DSAA 2015

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

ISBN: (纸本)9781467382731

Feature selection is an important step in building predictive models for most real-world problems. One of the popular methods in feature selection is Lasso. However, it shows instability in selecting features when dealing with correlated features. In this work, we propose a new method that aims to increase the stability of Lasso by encouraging similarities between features based on their relatedness, which is captured via a feature covariance matrix. Besides modeling positive feature correlations, our method can also identify negative correlations between features. We propose a convex formulation for our model along with an alternating optimization algorithm that can learn the weights of the features as well as the relationship between them. Using both synthetic and real-world data, we show that the proposed method is more stable than Lasso and many state-of-the-art shrinkage and feature selection methods. Also, its predictive performance is comparable to other methods. © 2015 IEEE.

关键词： Feature Selection

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