Latent semantic analysis for text categorization using neural network
Knowledge-Based Systems
Genetic algorithm for text clustering based on latent semantic indexing
Computers & Mathematics with Applications
Supervised latent semantic indexing using adaptive sprinkling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Proceedings of the 18th ACM conference on Information and knowledge management
Best-effort semantic document search on GPUs
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Learning to rank with (a lot of) word features
Information Retrieval
A two-stage feature selection method for text categorization
Computers & Mathematics with Applications
Using natural language processing to improve document categorization with associative networks
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
An effective class-centroid-based dimension reduction method for text classification
Proceedings of the 22nd international conference on World Wide Web companion
Parallel Training of An Improved Neural Network for Text Categorization
International Journal of Parallel Programming
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Latent Semantic Indexing (LSI) is a successful technology in information retrieval (IR) which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. However, LSI is not optimal for document categorization tasks because it aims to find the most representative features for document representation rather than the most discriminative ones. In this paper, we propose Supervised LSI (SLSI) which selects the most discriminative basis vectors using the training data iteratively. The extracted vectors are then used to project the documents into a reduced dimensional space for better classification. Experimental evaluations show that the SLSI approach leads to dramatic dimension reduction while achieving good classification results.