Optimal Dimensionality Discriminant Analysis and Its Application to Image Recognition

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors Feiping Nie, Shiming Xiang, Yangqiu Song, Changshui Zhang
Journal/Conference Name 1st Workshop on Component Analysis Methods for Classification Clustering Modeling and Estimation Problems in Computer Vision (CVPR workshop)
Paper Category
Paper Abstract Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, linear discriminant analysis (LDA) is one of the most popular methods and has been successfully applied in many classification problems. However, there are several drawbacks in LDA. First, it suffers from the singularity problem, which makes it hard to preform. Second, LDA has the distribution assumption which may make it fail in applications where the distribution is more complex than Gaussian. Third, LDA can not determine the optimal dimensionality for discriminant analysis, which is an important issue but has often been neglected previously. In this paper, we propose a new algorithm and endeavor to solve all these three problems. Furthermore, we present that our method can be extended to the two-dimensional case, in which the optimal dimensionalities of the two projection matrices can be determined simultaneously. Experimental results show that our methods are effective and demonstrate much higher performance in comparison to LDA.
Date of publication 2007
Code Programming Language MATLAB
Comment

Copyright Researcher II 2021