A new optimization criterion for generalized discriminant analysis on undersampled problems
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IDR/QR: an incremental dimension reduction algorithm via QR decomposition
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature extraction via generalized uncorrelated linear discriminant analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dimension Reduction in Text Classification with Support Vector Machines
The Journal of Machine Learning Research
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
IEEE Transactions on Knowledge and Data Engineering
Adaptive Nonlinear Discriminant Analysis by Regularized Minimum Squared Errors
IEEE Transactions on Knowledge and Data Engineering
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Multiclass classifiers based on dimension reduction with generalized LDA
Pattern Recognition
A comparison of generalized linear discriminant analysis algorithms
Pattern Recognition
Two-stage optimal component analysis
Computer Vision and Image Understanding
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Identification of signatures in biomedical spectra using domain knowledge
Artificial Intelligence in Medicine
Multiclass probabilistic kernel discriminant analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A new and fast implementation for null space based linear discriminant analysis
Pattern Recognition
Efficient GSVD based multi-user MIMO linear precoding and antenna selection scheme
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Regularized Discriminant Analysis, Ridge Regression and Beyond
The Journal of Machine Learning Research
Linear projection methods in face recognition under unconstrained illuminations: a comparative study
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Uncorrelated trace ratio linear discriminant analysis for undersampled problems
Pattern Recognition Letters
Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis
SIAM Journal on Matrix Analysis and Applications
A New and Fast Orthogonal Linear Discriminant Analysis on Undersampled Problems
SIAM Journal on Scientific Computing
Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition
Neural Processing Letters
Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Weighted generalized kernel discriminant analysis using fuzzy memberships
WSEAS Transactions on Mathematics
Feature extraction using a fast null space based linear discriminant analysis algorithm
Information Sciences: an International Journal
Regularized orthogonal linear discriminant analysis
Pattern Recognition
Using Clustering and Metric Learning to Improve Science Return of Remote Sensed Imagery
ACM Transactions on Intelligent Systems and Technology (TIST)
Novel Fisher discriminant classifiers
Pattern Recognition
Computational Optimization and Applications
Equivalence Between LDA/QR and Direct LDA
International Journal of Cognitive Informatics and Natural Intelligence
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In today's vector space information retrieval systems, dimension reduction is imperative for efficiently manipulating the massive quantity of data. To be useful, this lower-dimensional representation must be a good approximation of the full document set. To that end, we adapt and extend the discriminant analysis projection used in pattern recognition. This projection preserves cluster structure by maximizing the scatter between clusters while minimizing the scatter within clusters. A common limitation of trace optimization in discriminant analysis is that one of the scatter matrices must be nonsingular, which restricts its application to document sets in which the number of terms does not exceed the number of documents. We show that by using the generalized singular value decomposition (GSVD), we can achieve the same goal regardless of the relative dimensions of the term-document matrix. In addition, applying the GSVD allows us to avoid the explicit formation of the scatter matrices in favor of working directly with the data matrix, thus improving the numerical properties of the approach. Finally, we present experimental results that confirm the effectiveness of our approach.