-------- Original-Nachricht -------- Betreff: [computational.science] KDD Workshop on Data Mining using Matrices and Tensors Datum: Sat, 31 May 2008 19:33:31 -0700 Von: Chris Ding chqding@uta.edu Organisation: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com

*KDD 2008 Workshop on **Data Mining using Matrices and Tensors

*Workshop website: KDD 2008 Workshop on Data Mining Using Matrices and Tensors (DMMT08) http://www.cs.fiu.edu/%7Etaoli/kdd08-workshop/workshop.htm

Held in conjunction with The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining http://www.sigkdd.org/kdd2008/ (KDD 2008) http://www.sigkdd.org/kdd2008/

August 24, 2008, Las Vegas, USA

The field of pattern recognition, data mining and machine learning increasingly adapt methods and algorithms from advanced matrix computations, graph theory and optimization. Prominent examples are spectral clustering, non-negative matrix factorization, Principal component analysis (PCA) and Singular Value Decomposition (SVD) related clustering and dimension reduction, tensor analysis, L-1 regularization, etc. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement; they are especially suitable for parallel and distributed-memory computers to solve large scale challenging problems such as searching and extracting patterns from the entire Web. Hence the area of data mining using matrices and tensors is a popular and growing are of research activities.

This workshop will present recent advances in algorithms and methods using matrix and scientific computing/applied mathematics for modeling and analyzing massive, high-dimensional, and nonlinear-structured data. One main goal of the workshop is to bring together leading researchers on many topic areas (e.g., computer scientists, computational and applied mathematicians) to assess the state-of-the-art, share ideas, and form collaborations. We also wish to attract practitioners who seek novel ideas for applications. In summary, this workshop will strive to emphasize the following aspects:

* Presenting recent advances in algorithms and methods using matrix and scientific computing/applied mathematics * Addressing the fundamental challenges in data mining using matrices and tensors * Identifying killer applications and key industry drivers (where theories and applications meet) * Fostering interactions among researchers (from different backgrounds) sharing the same interest to promote cross-fertilization of ideas. * Exploring benchmark data for better evaluation of the techniques

Topic areas for the workshop include (but are not limited to) the following:

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Methods and algorithms:

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* Principal Component Analysis and Singular value decomposition for clustering and dimension reduction * Nonnegative matrix factorization for unsupervised and semi-supervised learning * Spectral graph clustering * L-1 Regularization and Sparsification * Sparse PCA and SVD * Randomized algorithms for matrix computation * Web search and ranking algorithms * Canonical Decompositions (CANDECOMP/PARAFAC) * Tensor analysis: Rank-1 Decomposition, PARAFAC/CANDECOMP, GLRAM/2DSVD, Tucker decompositions (e.g., the Higher-Order SVD) * GSVD for classification * Latent Semantic Indexing and other developments for Information Retrieval * Linear, quadratic and semi-definite Programming * Non-linear manifold learning and dimension reduction * Computational statistics involving matrix computations * Feature selection and extraction * Graph-based learning (classification, semi-supervised learning and unsupervised learning)

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*Application areas*

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* Information search and extraction from Web * Text processing and information retrieval * Image processing and analysis * Genomics and Bioinformatics * Scientific computing and computational sciences * Social Networks

Deadline and Workshop dates

* *June 10, 2008*: Electronic submission of full papers * * June 17, 2008 *: Author notification * * June 20, 2008*: Submission of Camera-ready papers * *August 24, 2008*: Workshop in Las Vegas, USA

Organiziers:

Chris Ding, University of Texas at Arlington, USA Tao Li, Florida International University, USA

Program Committee :

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Tammy Kolda, Sandia National Labs

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Jesse Barlow, Penn State University

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Michael Berry, University of Tennessee

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Yun Chi, NEC Laboratories America

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Lars Elden, Linkping University, Sweden

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Christos Faloutsos, Carnegie Mellon University

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Estratis Gallopoulos, University of Patras

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Joydeep Ghosh, University of Texas at Austin

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Ming Gu, University of Califonia, Berkeley

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Michael Jordan, University of California, Berkeley

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Yuanqing Lin, University of Pennsylvania

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Huan Liu, Arizona State University

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Michael Ng, Hong Kong Baptist University

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Haesun Park, Georgia Tech

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Wei Peng, Xerox Research

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Robert Plemmons, Wake Forest

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Alex Pothen, Old Domino University

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Yousef Saad, University of Minnesota

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Horst Simon, Lawrence Berkeley National Laboratory

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Fei Wang, Tsinghua University

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Jieping Ye, Arizona State University

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Kai Yu, NEC Laboratories America

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Hongyuan Zha, Georgia Tech

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