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Clustering patient medical records via sparse subspace representation

17th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2013

作者： Saha, Budhaditya Pham, Duc-Son Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong VIC Australia Institute for Multi-sensor Processing and Content Analysis Department of Computing Curtin University WA Australia

ISBN: (纸本)9783642374555

The health industry is facing increasing challenge with "big data" as traditional methods fail to manage the scale and complexity. This paper examines clustering of patient records for chronic diseases to facilitate a better construction of care plans. We solve this problem under the framework of subspace clustering. Our novel contribution lies in the exploitation of sparse representation to discover subspaces automatically and a domain-specific construction of weighting matrices for patient records. We show the new formulation is readily solved by extending existing 1 -regularized optimization algorithms. Using a cohort of both diabetes and stroke data we show that we outperform existing benchmark clustering techniques in the literature. © Springer-Verlag 2013.

关键词： Clustering algorithms

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Ontology mapping and reasoning in semantic time series processing 13

Ontology mapping and reasoning in semantic time series proce...

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15th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2013

作者： Bozić, Bojan Winiwarter, Werner Austrian Institute of Technology Department of Safety and Security Donau-City-Straße 1 Vienna Austria University of Vienna Research Group Data Analytics and Computing Waehringer Straße 29 Vienna Austria

ISBN: (纸本)9781450321136

After introducing the new field of Semantic Time Series Processing, we take our findings one step further and investigate ontology mapping and reasoning in semantic time series processing. This paper shows how ontologies for time series from different areas can be mapped together and how additional meta-information can be generated by using reasoning methods. For ontology mapping we use the bridging concept and present a general time series ontology, which allows the integration of new domain ontologies. In reasoning a popular open source reasoner is used to show generation of new time series meta-information. Additionally, we discuss our results together with a description of the validation for all previously introduced methods. © 2013 ACM.

关键词： Ontology

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ASSIST: Access controlled ship identification streams

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2013年 8290 LNCS卷 1-25页

作者： Cao, Jianneng Kister, Thomas Xiang, Shili Malhotra, Baljeet Tan, Wee-Juan Tan, Kian-Lee Bressan, Stéphane Cyber Center Purdue University West Lafayette IN United States School of Computing National University of Singapore Singapore Singapore Data Analytics Department Institute for Infocomm Research Singapore Singapore SAP Research Singapore Singapore Centre for Remote Imaging Sensing and Processing National University of Singapore Singapore Singapore

ISBN: (纸本)9783642452680

The International Maritime Organization (IMO) requires a majority of cargo and passenger ships to use the Automatic Identification System (AIS) for navigation safety and traffic control. Distributing live AIS data on the Internet can offer a global view for both operational and analytical purposes to port authorities, shipping and insurance companies, cargo owners and ship captains and other stakeholders. Yet, uncontrolled, this distribution can seriously undermine navigation safety and security and the privacy of the various stakeholders. In this paper we present ASSIST, an application system based on our recently proposed access control framework, to protect streaming data from unauthorized access. Furthermore, we have implemented ASSIST on top of StreamInsight, a commercial stream engine. The extensive experimental results show that our solution is effective and efficient. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Access control

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Multi-modal abnormality detection in video with unknown data segmentation

Multi-modal abnormality detection in video with unknown data...

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21st International Conference on Pattern Recognition, ICPR 2012

作者： Nguyen, Tien Vu Phung, Dinh Rana, Santu Pham, Duc Son Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University VIC Australia Department of Computing Curtin University WA Australia

ISBN: (纸本)9784990644109

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras;and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days. © 2012 ICPR Org Committee.

关键词： Pattern recognition

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Cumulative restricted boltzmann machines for ordinal matrix data analysis

Cumulative restricted boltzmann machines for ordinal matrix ...

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4th Asian Conference on Machine Learning, ACML 2012

作者： Tran, Truyen Phung, Dinh Venkatesh, Svetha Pattern Recognition and Data Analytics Deakin University Waurn Ponds VIC 3216 Australia Department of Computing Curtin University Bentley WA 6102 Australia

Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent opinion profile of citizens around the world, and is competitive against state-of-art collaborative filtering techniques on large-scale public datasets. The model thus has the potential to extend application of RBMs to diverse domains such as recommendation systems, product reviews and expert assessments. © 2012 T. Tran, D. Phung & S. Venkatesh.

关键词： Matrix algebra

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Learning from ordered sets and applications in collaborative ranking

Learning from ordered sets and applications in collaborative...

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4th Asian Conference on Machine Learning, ACML 2012

Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!/2)6:93145N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the statespace efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods. © 2012 T. Tran, D. Phung & S. Venkatesh.

关键词： Collaborative filtering

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Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach 12

Large-scale statistical modeling of motion patterns: A Bayes...

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8th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2012

作者： Rana, Santu Phung, Dinh Pham, Sonny Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Waurn Ponds VIC 3216 Australia Department of Computing Curtin University Bentley WA 6102 Australia

ISBN: (纸本)9781450316606

We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection. © 2012 ACM.

关键词： Bayesian networks

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Community building based on semantic time series

Community building based on semantic time series

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14th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2012

作者： Božić, Bojan Winiwarter, Werner Austrian Institute of Technology Department of Safety and Security Donau-City-Straße 1 Vienna Austria University of Vienna Research Group Data Analytics and Computing Universitätsstraße 5 Vienna Austria

ISBN: (纸本)9781450313063

In this paper, we present a new approach of time series enrichment with semantics, and the usage of this technology for building various kinds of communities interested in time series data. The paper shows the problem of assigning time series data to the right party of interest and why this problem could not be solved so far. We demonstrate a new way of processing semantic time series and the consequential ability of addressing and creating target communities. The combination of time series processing and Semantic Web technologies leads us to a new powerful method of data processing and data generation, which offers completely new opportunities to the expert user. © 2012 ACM.

关键词： Semantic Web

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The AAAI-13 conference workshops

The AAAI-13 conference workshops

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作者： Agrawal, Vikas Archibald, Christopher Bhatt, Mehul Bui, Hung Cook, Diane J. Cortés, Juan Geib, Christopher Gogate, Vibhav Guesgen, Hans W. Jannach, Dietmar Johanson, Michael Kersting, Kristian Konidaris, George Kotthoff, Lars Michalowski, Martin Natarajan, Sriraam O'Sullivan, Barry Pickett, Marc Podobnik, Vedran Poole, David Shastri, Lokendra Shehu, Amarda Sukthankar, Gita IBM Research India Mississippi State University United States University of Bremen Germany Laboratory for Natural Language Understanding Sunnyvale CA United States Washington State University United States University of Toulouse France Drexel University United States Department of Computer Science University of Texas Dallas United States Massey University New Zealand TU Dortmund Germany University of Alberta in Edmonton Canada Fraunhofer IAIS and the University of Bonn Germany MIT United States INSIGHT Centre for Data Analytics University College Cork Ireland Adventium Labs Minneapolis MN United States Indiana University Bloomington IN United States Naval Research Laboratory Washington DC United States Telecommunication Department of the Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of Computer Science University of British Columbia Vancouver Canada Infosys India George Mason University United States University of Central Florida United States

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14-15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05);Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06);Combining Constraint Solving with Mining and Learning (WS-13-07);Computer Poker and Imperfect Information (WS-13-08);Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09);Intelligent Robotic Systems (WS-13-10);Intelligent Techniques for Web Personalization and Recommendation (WS- 13-11);Learning Rich Representations from Low-Level Sensors (WS-13-12);Plan, Activity, and Intent Recognition (WS-13-13);Space, Time, and Ambient Intelligence (WS-13-14);Trading Agent Design and Analysis (WS-13-15);and Statistical Relational Artificial Intelligence (WS-13-16). Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： data mining

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Multi-modal abnormality detection in video with unknown data segmentation

Multi-modal abnormality detection in video with unknown data...

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International Conference on Pattern Recognition

作者： Tien Vu Nguyen Dinh Phung Santu Rana Duc Son Pham Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University VIC Australia Department of Computing Curtin University WA Australia

ISBN: (纸本)9781467322164

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras; and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days.

关键词： Hidden Markov models Vectors data models Computational modeling Cameras Detectors Surveillance

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