Title :
A new approach to dimensionality reduction based on locality preserving LDA
Author :
Di Zhang ; Jiazhong He
Author_Institution :
Sch. of Inf. Eng., Guangdong Med. Coll., Dongguan, China
Abstract :
Linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction (DR) techniques. However, LDA only captures global geometrical structure information of the data and ignores the geometrical variation of local data points of the same class. In this paper, a new supervised DR algorithm called local intraclass variation preserving LDA (LIPLDA) is proposed. We also show that the proposed algorithm can be extended to non-linear DR scenarios by applying the kernel trick.
Keywords :
data reduction; LIPLDA; dimensionality reduction; global geometrical structure information; kernel trick; linear discriminant analysis; local intraclass variation preserving LDA; locality preserving LDA; nonlinear DR scenarios; supervised dimensionality reduction techniques; Algorithm design and analysis; Classification algorithms; Eigenvalues and eigenfunctions; Error analysis; Kernel; Principal component analysis; Training data; dimensionality reduction; linear discriminant analysis; locality preserving projection; pattern classification;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/FSKD.2013.6816254
