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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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Continuous discovery of co-location contexts from Bluetooth data

Continuous discovery of co-location contexts from Bluetooth ...

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

The discovery of contexts is important for context-aware applications in pervasive computing. This is a challenging problem because of the stream nature of data, the complexity and changing nature of contexts. We propose a Bayesian nonparametric model for the detection of co-location contexts from Bluetooth signals. By using an Indian buffet process as the prior distribution, the model can discover the number of contexts automatically. We introduce a novel fixed-lag particle filter that processes data incrementally. This sampling scheme is especially suitable for pervasive computing as the computational requirements remain constant in spite of growing data. We examine our model on a synthetic dataset and two real world datasets. To verify the discovered contexts, we compare them to the communities detected by the Louvain method, showing a strong correlation between the results of the two methods. Fixed-lag particle filter is compared with Gibbs sampling in terms of the normalized factorization error that shows a close performance between the two inference methods. As fixed-lag particle filter processes a small chunk of data when it comes and does not need to be restarted, its execution time is significantly shorter than that of Gibbs sampling. © 2015 Elsevier B.V. All rights reserved.

关键词： Ubiquitous computing

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An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal

An EEMD-PCA approach to extract heart rate, respiratory rate...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mohammod Abdul Motin Chandan Kumar Karmakar Marimuthu Palaniswami Department of Electrical & Electronic Engineering University of Melbourne Melbourne Australia Centre of Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong Australia

The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 45 epochs of PPG, electrocardiogram (ECG) and respiratory signal extracted from the MIMIC database (Physionet ATM data bank). The ECG and capnograph based respiratory signal were used as the ground truth and several metrics such as magnitude squared coherence (MSC), correlation coefficients (CC) and root mean square (RMS) error were used to compare the performance of EEMD-PCA algorithm with most of the existing methods in the literature. Results of EEMD-PCA based extraction of HR, RR and RA from PPG signal showed that the median RMS error (quartiles) obtained for RR was 0 (0, 0.89) breaths/min, for HR was 0.62 (0.56, 0.66) beats/min and for RA the average value of MSC and CC was 0.95 and 0.89 respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR, RR and RA than other existing methods.

关键词： principal component analysis biological tissues blood cardiovascular system feature extraction mean square error methods medical signal processing oximetry photoplethysmography Mean square error Heart Rate Photoplethysmography medical signal processing Oximetry SRGN gene TISSUES (BIOLOGY) Cardiovascular System Feature extraction principal components analysis Right Ascension Vital Signs signals Root mean square Blood

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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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Bayesian nonparametric Multiple Instance Regression

Bayesian nonparametric Multiple Instance Regression

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International Conference on pattern recognition

作者： Saravanan Subramanian Santu Rana Sunil Gupta P. Bagavathi Sivakumar C. Shunmuga Velayutham Svetha Venkateshc Centre for Pattern Recognition and Data Analytics Deakin University Australia Dept of Computer Science and Engineering Amrita University Coimbatore India Deakin University Burwood VIC AU

ISBN: (纸本)9781509048489

Multiple Instance Regression jointly models a set of instances and its corresponding real-valued output. We present a novel multiple instance regression model that infers a subset of instances in each bag that best describes the bag label and uses them to learn a predictive model in a unified framework. We assume that instances in each bag are drawn from a mixture distribution and thus naturally form groups, and instances from one of this group explain the bag label. The largest cluster is assumed to be correlated with the label. We evaluate this model on the crop yield prediction and aerosol depth prediction problems. The predictive accuracy of our model is better than the state of the art MIR methods.

关键词： Predictive models Agriculture Prediction algorithms Bayes methods data models Computational modeling Clustering algorithms

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Multi-task transfer learning for in-hospital-death prediction of ICU patients

Multi-task transfer learning for in-hospital-death predictio...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Chandan Karmakar Budhaditya Saha Marimuthu Palaniswami Svetha Venkatesh Centre of Pattern Recognition and Data Analytics (PRaDA) Deakin University Gee-long VIC Australia Department of Electrical & Electronic Engineering The University of Melbourne Melbourne VIC Australia

Multi-Task Transfer Learning (MTTL) is an efficient approach for learning from inter-related tasks with small sample size and imbalanced class distribution. Since the intensive care unit (ICU) data set (publicly available in Physionet) has subjects from four different ICU types, we hypothesize that there is an underlying relatedness amongst various ICU types. Therefore, this study aims to explore MTTL model for in-hospital mortality prediction of ICU patients. We used single-task learning (STL) approach on the augmented data as well as individual ICU data and compared the performance with the proposed MTTL model. As a performance measurement metrics, we used sensitivity (Sens), positive predictivity (+Pred), and Score. MTTL with class balancing showed the best performance with score of 0.78, 0.73, o.52 and 0.63 for ICU type 1 (Coronary care unit), 2 (Cardiac surgery unit), 3 (Medical ICU) and 4 (Surgical ICU) respectively. In contrast the maximum score obtained using STL approach was 0.40 for ICU type 1 & 2. These results indicates that the performance of in-hospital mortality can be improved using ICU type information and by balancing the `non-survivor' class. The findings of the study may be useful for quantifying the quality of ICU care, managing ICU resources and selecting appropriate interventions.

关键词： data models Feature extraction Predictive models Surgery Measurement Computational modeling Covariance matrices

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What shall i share and with Whom? - A multi-task learning formulation using multi-faceted task relationships

What shall i share and with Whom? - A multi-task learning fo...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Gupta, Sunil Rana, Santu Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Geelong Waurn Ponds Campus VIC Australia

ISBN: (纸本)9781510811522

Multi-task learning is a learning paradigm that improves the performance of "related" tasks through their joint learning. To do this each task answers the question "Which other task should I share with"? This task relatedness can be complex - a task may be related to one set of tasks based on one subset of features and to other tasks based on other subsets. Existing multi-task learning methods do not explicitly model this reality, learning a single-faceted task relationship over all the features. This degrades performance by forcing a task to become similar to other tasks even on their unrelated features. Addressing this gap, we propose a novel multi-task learning model that leams multi-faceted task relationship, allowing tasks to collaborate differentially on different feature subsets. This is achieved by simultaneously learning a low dimensional sub-space for task parameters and inducing task groups over each latent subspace basis using a novel combination of L1 and pairwise L∞ norms. Further, our model can induce grouping across both positively and negatively related tasks, which helps towards exploiting knowledge from all types of related tasks. We validate our model on two synthetic and five real datasets, and show significant performance improvements over several state-of-the-art multi-task learning techniques. Thus our model effectively answers for each task: What shall I share and with whom?. Copyright © SIAM.

关键词： Learning systems

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Analysing the History of Autism Spectrum Disorder Using Topic Models

Analysing the History of Autism Spectrum Disorder Using Topi...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Adham Beykikhoshk Dinh Phung Ognjen Arandjelović Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia School of Computer Science University of St Andrews Fife Scotland United Kingdom University of Saint Andrews Saint Andrews Fife GB

ISBN: (纸本)9781509052073

We describe a novel framework for the discovery of underlying topics of a longitudinal collection of scholarly data, and the tracking of their lifetime and popularity over time. Unlike the social media or news data where the underlying topics evolve over time, the topic nuances in science result in new scientific directions to emerge. Therefore, we model the longitudinal literature data with a new approach that uses topics which remain identifiable over the course of time. Current studies either disregard the time dimension or treat it as an exchangeable covariate when they fix the topics over time or do not share the topics over epochs when they model the time naturally. We address these issues by adopting a non-parametric Bayesian approach. We assume the data is partially exchangeable and divide it into consecutive epochs. Then, by fixing the topics in a recurrent Chinese restaurant franchise, we impose a static topical structure on the corpus such that the topics are shared across epochs and the documents within epochs. We demonstrate the effectiveness of the proposed framework on a collection of medical literature related to autism spectrum disorder. We collect a large corpus of publications and carefully examine two important research issues of the domain as case studies. Moreover, we make the results of our experiment and the source code of the model, freely available to the public. This aids other researchers to analyse our results or apply the model to their data collections.

关键词： data models Autism Computational modeling Analytical models Atomic measurements Probability distribution Probabilistic logic

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Multi-View Subspace Clustering for Face Images

Multi-View Subspace Clustering for Face Images

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International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015

作者： Zhang, Xin Phung, Dinh Venkatesh, Svetha Pham, Duc-Son Liu, Wanquan Pattern Recognition and Data Analytics Deakin University Australia Department of Computing Curtin University PerthWA Australia

ISBN: (纸本)9781467367950

In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary information to each other. One recent and powerful computer vision method for clustering is sparse subspace clustering (SSC);however, it was not designed for multi-view data, which break down its linear separability assumption. To integrate complementary information between views, multi-view clustering algorithms are required to improve the clustering performance. In this paper, we propose a novel multi-view subspace clustering by searching for an unified latent structure as a global affinity matrix in subspace clustering. Due to the integration of affinity matrices for each view, this global affinity matrix can best represent the relationship between clusters. This could help us achieve better performance on face clustering. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other alternatives based on state-of-The-Arts on challenging multi-view face datasets. © 2015 IEEE.

关键词： Clustering algorithms

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