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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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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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The adaptable buffer algorithm for high quantile estimation in non-stationary data streams

The adaptable buffer algorithm for high quantile estimation ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ognjen Arandjelović Duc-Son Pham Svetha Venkatesh Centre for Pattern Recognition & Data Analytics Deakin University Geelong VIC Australia Department of Computing Curtin University Perth WA Australia

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semi-automatic surveillance analytics systems which detect abnormalities in close-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the memory for storing observations is limited. We make several major contributions: (i) we derive an important theoretical result which shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data, (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings, (iii) we introduce a novel algorithm which implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity, and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic data sets and three large `real-world' streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high valued and the available buffer capacity severely limited.

关键词： Australia Indexes Entropy

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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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Bayesian functional optimisation with shape prior

arXiv

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arXiv 2018年

作者： Vellanki, Pratibha Rana, Santu Gupta, Sunil de Celis Leal Alessandra Sutti, David Rubin Height, Murray Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Institute for Frontier Materials GTP Research Deakin University Geelong Australia

Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments. Experimental processes often involve control variables that changes over time. Such problems can be formulated as a functional optimisation problem. We develop a novel Bayesian optimisation framework for such functional optimisation of expensive black-box processes. We represent the control function using Bernstein polynomial basis and optimise in the coefficient space. We derive the theory and practice required to dynamically adjust the order of the polynomial degree, and show how prior information about shape can be integrated. We demonstrate the effectiveness of our approach for short polymer fibre design and optimising learning rate schedules for deep networks. Copyright © 2018, The Authors. All rights reserved.

关键词： Process control

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The relationship between linguistic expression and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study of blog content

arXiv

引用

arXiv 2018年

作者： O'Dea, B. Boonstra, T.W. Larsen, M.E. Nguyen, T. Venkatesh, S. Christensen, H. Black Dog Institute Faculty of Medicine University of New South Wales NSW Australia Centre for Pattern Recognition and Data Analytics Deakin University Victoria Australia

Introduction: Due to its popularity and availability, social media data may present a new way to identify individuals who are experiencing mental illness. By analysing individuals' blog content, this study aimed to investigate the associations between linguistic features and symptoms of depression, generalised anxiety, and suicidal ideation. Methods: This study utilised a longitudinal study design. Individuals who blogged were invited to participate in a study in which they completed fortnightly mental health questionnaires (PHQ-9, GAD-7) for a period of 36 weeks. Linguistic features were extracted from blog data using the LIWC tool. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and mental health scores between subjects. We then used the multivariate regression model to predict longitudinal changes in mood within subjects. Results: A total of 153 participants consented to taking part, with 38 participants completing the required number of questionnaires and blog posts during the study period. Between-subject analysis revealed that several linguistic features, including tentativeness and non-fluencies, were significantly associated with depression and anxiety symptoms, but not suicidal thoughts. Within-subject analysis showed no robust correlations between linguistic features and changes in mental health score. Discussion: This study provides further support for the relationship between linguistic features within social media data and individuals' symptoms of depression and anxiety. The lack of robust within-subject correlations indicate that the relationship observed at the group level may not generalise to individual changes over time. Copyright © 2018, The Authors. All rights reserved.

关键词： Blogs

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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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$\mathtt {Deepr}$: 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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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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