Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Regularized discriminant analysis for high dimensional, low sample size data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised probabilistic principal component analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
A comparison of generalized linear discriminant analysis algorithms
Pattern Recognition
An adaptive nonparametric discriminant analysis method and its application to face recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Face recognition using kernel uncorrelated discriminant analysis
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Kernel uncorrelated discriminant analysis for radar target recognition
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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Feature extraction is important in many applications, such as text and image retrieval, because of high dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature extraction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, we will first propose the ULDA/QR algorithm to simplify the previous implementation of ULDA. Then we propose the ULDA/GSVD algorithm, based on a novel optimization criterion, to address the singularity problem. It is applicable for undersampled problem, where the data dimension is much larger than the data size, such as text and image retrieval. The novel criterion used in ULDA/GSVD is the perturbed version of the one from ULDA/QR, while surprisingly, the solution to ULDA/GSVD is shown to be independent of the amount of perturbation applied. We did extensive experiments on text and face image data to show the effectiveness of ULDA/GSVD and compare with other popular feature extraction algorithms.