An Optimal Transformation for Discriminant and Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The Journal of Machine Learning Research
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Efficient model selection for regularized linear discriminant analysis
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
Semi-supervised orthogonal discriminant analysis via label propagation
Pattern Recognition
Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis
SIAM Journal on Matrix Analysis and Applications
An optimization criterion for generalized discriminant analysis on undersampled problems
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
Computational Optimization and Applications
A Rayleigh-Ritz style method for large-scale discriminant analysis
Pattern Recognition
| Hi-index | 0.10 |
For linear discriminant analysis (LDA), the ratio trace and trace ratio are two basic criteria generalized from the classical Fisher criterion function, while the orthogonal and uncorrelated constraints are two common conditions imposed on the optimal linear transformation. The ratio trace criterion with both the orthogonal and uncorrelated constraints have been extensively studied in the literature, whereas the trace ratio criterion receives less interest mainly due to the lack of a closed-form solution and efficient algorithms. In this paper, we make an extensive study on the uncorrelated trace ratio linear discriminant analysis, with particular emphasis on the application on the undersampled problem. Two regularization uncorrelated trace ratio LDA models are discussed for which the global solutions are characterized and efficient algorithms are established. Experimental comparison on several LDA approaches are conducted on several real world datasets, and the results show that the uncorrelated trace ratio LDA is competitive with the orthogonal trace ratio LDA, but is better than the results based on ratio trace criteria in terms of the classification performance.