Matrix computations (3rd ed.)
A semidiscrete matrix decomposition for latent semantic indexing information retrieval
ACM Transactions on Information Systems (TOIS)
Algebraic Techniques for Analysis of Large Discrete-Valued Datasets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
PROXIMUS: a framework for analyzing very high dimensional discrete-attributed datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Compression, Clustering, and Pattern Discovery in Very High-Dimensional Discrete-Attribute Data Sets
IEEE Transactions on Knowledge and Data Engineering
Structure in the Enron Email Dataset
Computational & Mathematical Organization Theory
Nonorthogonal decomposition of binary matrices for bounded-error data compression and analysis
ACM Transactions on Mathematical Software (TOMS)
Full regularization path for sparse principal component analysis
Proceedings of the 24th international conference on Machine learning
Representing Images Using Nonorthogonal Haar-Like Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic indexing in structured peer-to-peer networks
Journal of Parallel and Distributed Computing
Pattern Discovery for High-Dimensional Binary Datasets
Neural Information Processing
Bars problem solving - new neural network method and comparison
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Hypergraph-based multilevel matrix approximation for text information retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A study of semi-discrete matrix decomposition for LSI in automated text categorization
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Discrete Eckart-Young Theorem for Integer Matrices
SIAM Journal on Matrix Analysis and Applications
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We present algorithms for computing a semidiscrete approximation to a matrix in a weighted norm, with the Frobenius norm as a special case. The approximation is formed as a weighted sum of outer products of vectors whose elements are ±1 or 0, so the storage required by the approximation is quite small. We also present a related algorithm for approximation of a tensor. Applications of the algorithms are presented to data compression, filtering, and information retrieval; software is provided in C and in Matlab.