Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A vector space model for automatic indexing
Communications of the ACM
Modern Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new covariance estimate for Bayesian classifiers in biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes a Discriminative Semantic Feature (DSF) method for vector space model based text categorization. The DSF method, which involves two stages, first reduces the dimension of the document vector space by Latent Semantic Indexing (LSI), and then applies a Robust linear Discriminant analysis Model (RDM), which improves the classical LDA by a energy-adaptive regularization criteria, to extract the discriminative semantic feature with enhanced discrimination power. As a result, DSF method can not only uncover latent semantic structure but also capture the discriminative feature. Comparative experiments on various state-of-art dimension reduction schemes such as our DSF, LSI, orthogonal centroid, two-stage LSI+LDA, LDA/QR and LDA/GSVD, are also performed. Experiments using the Reuters-21578 text collection show the proposed method performs better than other algorithms.