Understanding search engines: mathematical modeling and text retrieval
Understanding search engines: mathematical modeling and text retrieval
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical text categorization and its application to bioinformatics
Hierarchical text categorization and its application to bioinformatics
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Fast dimension reduction based on NMF
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Subtractive clustering for seeding non-negative matrix factorizations
Information Sciences: an International Journal
Hi-index | 0.00 |
Identifying functional groups of genes is a challenging problem for biological applications. Text mining approaches can be used to build hierarchical clusters or trees from the information in the biological literature. In particular, the nonnegative matrix factorization (NMF) is examined as one approach to label hierarchical trees. A generic labeling algorithm as well as an evaluation technique is proposed, and the effects of different NMF parameters with regard to convergence and labeling accuracy are discussed. The primary goals of this study are to provide a qualitative assessment of the NMF and its various parameters and initialization, to provide an automated way to classify biomedical data, and to provide a method for evaluating labeled data assuming a static input tree. As a byproduct, a method for generating gold standard trees is proposed.