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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Using Discriminant Analysis for Multi-class Classification
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we propose a new discriminant analysis, called linear boundary discriminant analysis (LBDA), which increases class separability by reflecting the different significances of non-boundary and boundary patterns. This is achieved by defining two novel scatter matrices and solving the eigenproblem on the criterion described by these scatter matrices. As a result, the classification performance using the extracted features can be improved. This effectiveness of the LBDA is theoretically explained by reformulating the scatter matrices in pairwise form. Experiments are conducted to show the performance of LBDA, and the results show that LBDA can perform better than other algorithms in most cases.