DocumentCode :
1124698
Title :
Fast Algorithm for Updating the Discriminant Vectors of Dual-Space LDA
Author :
Zheng, Wenming ; Tang, Xiaoou
Author_Institution :
Key Lab. of Child Dev. & Learning Sci., Southeast Univ., Nanjing, China
Volume :
4
Issue :
3
fYear :
2009
Firstpage :
418
Lastpage :
427
Abstract :
Dual-space linear discriminant analysis (DSLDA) is a popular method for discriminant analysis. The basic idea of the DSLDA method is to divide the whole data space into two complementary subspaces, i.e., the range space of the within-class scatter matrix and its complementary space, and then solve the discriminant vectors in each subspace. Hence, the DSLDA method can take full advantage of the discriminant information of the training samples. However, from the computational point of view, the original DSLDA method may not be suitable for online training problems because of its heavy computational cost. To this end, we modify the original DSLDA method and then propose a data order independent incremental algorithm to accurately update the discriminant vectors of the DSLDA method when new samples are inserted into the training data set. We conduct experiments on the AR face database to confirm the better performance of the proposed algorithms in terms of the recognition accuracy and computational efficiency.
Keywords :
S-matrix theory; feature extraction; image recognition; visual databases; AR face database; complementary space; data order independent incremental algorithm; data set training; dual-space linear discriminant analysis; feature extraction; online training problem; scatter matrix; Dual-space linear discriminant analysis (DSLDA); feature extraction; incremental linear discriminant analysis;
fLanguage :
English
Journal_Title :
Information Forensics and Security, IEEE Transactions on
Publisher :
ieee
ISSN :
1556-6013
Type :
jour
DOI :
10.1109/TIFS.2009.2025844
Filename :
5153281
Link To Document :
بازگشت