Understanding search engines: mathematical modeling and text retrieval
Understanding search engines: mathematical modeling and text retrieval
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Email Surveillance Using Non-negative Matrix Factorization
Computational & Mathematical Organization Theory
Inference and evaluation of the multinomial mixture model for text clustering
Information Processing and Management: an International Journal
SVD based initialization: A head start for nonnegative matrix factorization
Pattern Recognition
Non-negative matrix factorization with α-divergence
Pattern Recognition Letters
Nonnegative matrix factorization with quadratic programming
Neurocomputing
Gene tree labeling using nonnegative matrix factorization on biomedical literature
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Structural Identifiability in Low-Rank Matrix Factorization
COCOON '08 Proceedings of the 14th annual international conference on Computing and Combinatorics
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
A new visual search interface for web browsing
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Nonnegative factor analysis for text document clustering
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Tumor clustering using nonnegative matrix factorization with gene selection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Nonnegative Matrix Factorization on Orthogonal Subspace
Pattern Recognition Letters
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A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for non-negative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies.