The Experts below are selected from a list of 419508 Experts worldwide ranked by ideXlab platform

Ming-ting Sun - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised action classification using space-time Link Analysis
    Eurasip Journal on Image and Video Processing, 2010
    Co-Authors: Haowei Liu, Rogerio S. Feris, Volker Krueger, Ming-ting Sun
    Abstract:

    We address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time Link Analysis approach which consistently matches or exceeds the performance of traditional unsupervised action categorization methods in various datasets. Our method is inspired by the recent success of Link Analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatiotemporal relationships between the video features, while leveraging the power of graph matching for action classification. We present a comprehensive set of experiments demonstrating that our approach is capable of handling cluttered backgrounds, activities with subtle movements, and video data from moving cameras. State-of-the-art results are reported on standard datasets. We also demonstrate our method in a compelling surveillance application with the goal of avoiding fraud in retail stores.

  • ISCAS - Unsupervised action classification using space-time Link Analysis
    Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010
    Co-Authors: Haowei Liu, Rogerio S. Feris, Volker Krüger, Ming-ting Sun
    Abstract:

    In this paper we address the problem of unsu-pervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time Link Analysis approach which matches the performance of traditional unsupervised action categorization methods in a standard dataset. Our method is inspired by the recent success of Link Analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatio-temporal relationships between the video features, while leveraging the power of graph matching for action classification. We present an experiment to demonstrate that our approach is capable of handling cluttered backgrounds, activities with subtle movements, and video data from moving cameras.

Michael I Jordan - One of the best experts on this subject based on the ideXlab platform.

  • stable algorithms for Link Analysis
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001
    Co-Authors: Alice X Zheng, Michael I Jordan
    Abstract:

    The Kleinberg HITS and the Google PageRank algorithms are eigenvector methods for identifying ``authoritative'' or ``influential'' articles, given hyperLink or citation information. That such algorithms should give reliable or consistent answers is surely a desideratum, and in~\cite{ijcaiPaper}, we analyzed when they can be expected to give stable rankings under small perturbations to the Linkage patterns. In this paper, we extend the Analysis and show how it gives insight into ways of designing stable Link Analysis methods. This in turn motivates two new algorithms, whose performance we study empirically using citation data and web hyperLink data.

  • SIGIR - Stable algorithms for Link Analysis
    Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '01, 2001
    Co-Authors: Alice X Zheng, Michael I Jordan
    Abstract:

    The Kleinberg HITS and the Google PageRank algorithms are eigenvector methods for identifying ``authoritative'' or ``influential'' articles, given hyperLink or citation information. That such algorithms should give reliable or consistent answers is surely a desideratum, and in~\cite{ijcaiPaper}, we analyzed when they can be expected to give stable rankings under small perturbations to the Linkage patterns. In this paper, we extend the Analysis and show how it gives insight into ways of designing stable Link Analysis methods. This in turn motivates two new algorithms, whose performance we study empirically using citation data and web hyperLink data.

Mike Thelwall - One of the best experts on this subject based on the ideXlab platform.

  • interpreting social science Link Analysis research a theoretical framework
    Journal of the Association for Information Science and Technology, 2006
    Co-Authors: Mike Thelwall
    Abstract:

    Link Analysis in various forms is now an established technique in many different subjects, reflecting the perceived importance of Links and of the Web. A critical but very difficult issue is how to interpret the results of social science Link analyses. It is argued that the dynamic nature of the Web, its lack of quality control, and the online proliferation of copying and imitation mean that methodologies operating within a highly positivist, quantitative framework are ineffective. Conversely, the sheer variety of the Web makes application of qualitative methodologies and pure reason very problematic to large-scale studies. Methodology triangulation is consequently advocated, in combination with a warning that the Web is incapable of giving definitive answers to large-scale Link Analysis research questions concerning social factors underlying Link creation. Finally, it is claimed that although theoretical frameworks are appropriate for guiding research, a Theory of Link Analysis is not possible. © 2006 Wiley Periodicals, Inc.

  • Interpreting social science Link Analysis research: A theoretical framework
    Journal of the American Society for Information Science and Technology, 2005
    Co-Authors: Mike Thelwall
    Abstract:

    Link Analysis in various forms is now an established technique in many different subjects, reflecting the perceived importance of Links and of the Web. A critical but very difficult issue is how to interpret the results of social science Link analyses. It is argued that the dynamic nature of the Web, its lack of quality control, and the online proliferation of copying and imitation mean that methodologies operating within a highly positivist, quantitative framework are ineffective. Conversely, the sheer variety of the Web makes application of qualitative methodologies and pure reason very problematic to large-scale studies. Methodology triangulation is consequently advocated, in combination with a warning that the Web is incapable of giving definitive answers to large-scale Link Analysis research questions concerning social factors underlying Link creation. Finally, it is claimed that although theoretical frameworks are appropriate for guiding research, a Theory of Link Analysis is not possible

  • Link Analysis an information science approach
    2004
    Co-Authors: Mike Thelwall
    Abstract:

    Part I: Theory. Introduction. Web Crawlers and Search Engines. The Theoretical Perspective for Link Counting. Interpreting Link counts: Random samples and correlations. Link structures in the web graph. The content structure of the web. Universities: Link types. Universities: Link models. Universities: International Links. Departments and subjects. Journals and articles. Search engines and web design. A health check for Spanish universities. Personal web pages Linking to universities. Academic networks. Business web sites. Using commercial search engines and the Internet Archive. Personal crawlers. Data cleansing. Online university Link databases. Embedded Link Analysis. Social Network Analysis. Network visualizations. Academic Link indicators. Summary. Glossary.

Zheng Chen - One of the best experts on this subject based on the ideXlab platform.

  • SIGIR - Exploiting the hierarchical structure for Link Analysis
    Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05, 2005
    Co-Authors: Guirong Xue, Qiang Yang, Huajun Zeng, Zheng Chen
    Abstract:

    Link Analysis algorithms have been extensively used in Web information retrieval. However, current Link Analysis algorithms generally work on a flat Link graph, ignoring the hierarchal structure of the Web graph. They often suffer from two problems: the sparsity of Link graph and biased ranking of newly-emerging pages. In this paper, we propose a novel ranking algorithm called Hierarchical Rank as a solution to these two problems, which considers both the hierarchical structure and the Link structure of the Web. In this algorithm, Web pages are first aggregated based on their hierarchical structure at directory, host or domain level and Link Analysis is performed on the aggregated graph. Then, the importance of each node on the aggregated graph is distributed to individual pages belong to the node based on the hierarchical structure. This algorithm allows the importance of Linked Web pages to be distributed in the Web page space even when the space is sparse and contains new pages. Experimental results on the .GOV collection of TREC 2003 and 2004 show that hierarchical ranking algorithm consistently outperforms other well-known ranking algorithms, including the PageRank, BlockRank and LayerRank. In addition, experimental results show that Link aggregation at the host level is much better than Link aggregation at either the domain or directory levels.

  • exploiting the hierarchical structure for Link Analysis
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005
    Co-Authors: Guirong Xue, Qiang Yang, Huajun Zeng, Zheng Chen
    Abstract:

    Link Analysis algorithms have been extensively used in Web information retrieval. However, current Link Analysis algorithms generally work on a flat Link graph, ignoring the hierarchal structure of the Web graph. They often suffer from two problems: the sparsity of Link graph and biased ranking of newly-emerging pages. In this paper, we propose a novel ranking algorithm called Hierarchical Rank as a solution to these two problems, which considers both the hierarchical structure and the Link structure of the Web. In this algorithm, Web pages are first aggregated based on their hierarchical structure at directory, host or domain level and Link Analysis is performed on the aggregated graph. Then, the importance of each node on the aggregated graph is distributed to individual pages belong to the node based on the hierarchical structure. This algorithm allows the importance of Linked Web pages to be distributed in the Web page space even when the space is sparse and contains new pages. Experimental results on the .GOV collection of TREC 2003 and 2004 show that hierarchical ranking algorithm consistently outperforms other well-known ranking algorithms, including the PageRank, BlockRank and LayerRank. In addition, experimental results show that Link aggregation at the host level is much better than Link aggregation at either the domain or directory levels.

  • Link fusion a unified Link Analysis framework for multi type interrelated data objects
    The Web Conference, 2004
    Co-Authors: Benyu Zhang, Zheng Chen, Shuicheng Yan, Edward A Fox
    Abstract:

    Web Link Analysis has proven to be a significant enhancement for quality based web search. Most existing Links can be classified into two categories: intra-type Links (e.g., web hyperLinks), which represent the relationship of data objects within a homogeneous data type (web pages), and inter-type Links (e.g., user browsing log) which represent the relationship of data objects across different data types (users and web pages). Unfortunately, most Link Analysis research only considers one type of Link. In this paper, we propose a unified Link Analysis framework, called "Link fusion", which considers both the inter- and intra- type Link structure among multiple-type inter-related data objects and brings order to objects in each data type at the same time. The PageRank and HITS algorithms are shown to be special cases of our unified Link Analysis framework. Experiments on an instantiation of the framework that makes use of the user data and web pages extracted from a proxy log show that our proposed algorithm could improve the search effectiveness over the HITS and DirectHit algorithms by 24.6% and 38.2% respectively.

  • WISE - A unified framework for Web Link Analysis
    Proceedings of the Third International Conference on Web Information Systems Engineering 2002. WISE 2002., 2002
    Co-Authors: Zheng Chen, Li Tao, Jidong Wang, Liu Wenyin
    Abstract:

    Web Link Analysis has been proved to significantly enhance the precision of Web searching in practice. Among existing approaches, Kleinberg's (1998) HITS and Google's PageRank are the two most representative algorithms that employ explicit hyperLink structure among Web pages to conduct Link Analysis, and DirectHit represents the other extreme that takes the user's access frequency as an implicit Link to the Web page for assessing its importance. We propose a novel Link Analysis algorithm which puts both explicit and implicit Link structures under a unified framework, and show that HITS and DirectHit are essentially two extreme instances of our proposed method. One important advantage of our method is its ability to analyze not only the hyperLinks between Web pages but also the interactions between users and the Web at the same time. The importance of Web pages and users can reinforce each other to improve Web Link Analysis. Compared with traditional HITS and DirectHit algorithms, our method further improves the search precision by 11.8% and 25.3%.

William B. Rouse - One of the best experts on this subject based on the ideXlab platform.

  • A Parameterized Approach to Spam-Resilient Link Analysis of the Web
    IEEE Transactions on Parallel and Distributed Systems, 2009
    Co-Authors: James Caverlee, Steve Webb, William B. Rouse
    Abstract:

    Link-based Analysis of the Web provides the basis for many important applications-like Web search, Web-based data mining, and Web page categorization-that bring order to the massive amount of distributed Web content. Due to the overwhelming reliance on these important applications, there is a rise in efforts to manipulate (or spam) the Link structure of the Web. In this manuscript, we present a parameterized framework for Link Analysis of the Web that promotes spam resilience through a source-centric view of the Web. We provide a rigorous study of the set of critical parameters that can impact source-centric Link Analysis and propose the novel notion of influence throttling for countering the influence of Link-based manipulation. Through formal Analysis and a large-scale experimental study, we show how different parameter settings may impact the time complexity, stability, and spam resilience of Web Link Analysis. Concretely, we find that the source-centric model supports more effective and robust rankings in comparison with existing Web algorithms such as PageRank.