KDD Workshop on Data Mining using Matrices and Tensors

> *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:
>
> *
>
> Methods and algorithms:
>
> *
>
> * 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)
>
> * *
>
> *Application areas*
>
> * *
>
> * 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 :
>
> *
>
> Tammy Kolda, Sandia National Labs
>
> *
>
> Jesse Barlow, Penn State University
>
> *
>
> Michael Berry, University of Tennessee
>
> *
>
> Yun Chi, NEC Laboratories America
>
> *
>
> Lars Elden, Linkping University, Sweden
>
> *
>
> Christos Faloutsos, Carnegie Mellon University
>
> *
>
> Estratis Gallopoulos, University of Patras
>
> *
>
> Joydeep Ghosh, University of Texas at Austin
>
> *
>
> Ming Gu, University of Califonia, Berkeley
>
> *
>
> Michael Jordan, University of California, Berkeley
>
> *
>
> Yuanqing Lin, University of Pennsylvania
>
> *
>
> Huan Liu, Arizona State University
>
> *
>
> Michael Ng, Hong Kong Baptist University
>
> *
>
> Haesun Park, Georgia Tech
>
> *
>
> Wei Peng, Xerox Research
>
> *
>
> Robert Plemmons, Wake Forest
>
> *
>
> Alex Pothen, Old Domino University
>
> *
>
> Yousef Saad, University of Minnesota
>
> *
>
> Horst Simon, Lawrence Berkeley National Laboratory
>
> *
>
> Fei Wang, Tsinghua University
>
> *
>
> Jieping Ye, Arizona State University
>
> *
>
> Kai Yu, NEC Laboratories America
>
> *
>
> Hongyuan Zha, Georgia Tech
>
>
Received on Mon Jun 2 07:58:27 2008