Title :
Joint kernel dictionary and classifier learning for sparse coding via locality preserving K-SVD
Author :
Weiyang Liu ; Zhiding Yu ; Meng Yang ; Lijia Lu ; Yuexian Zou
Author_Institution :
Sch. of ECE, Peking Univ., Beijing, China
fDate :
June 29 2015-July 3 2015
Abstract :
We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related methods works better in a more discriminative and separable space, we map the original feature space to the kernel space, where samples of different classes become more separable. Experimental results show the proposed approach has strong discrimination power and is comparable or outperforms some state-of-the-art approaches on public databases.
Keywords :
image classification; image coding; learning (artificial intelligence); singular value decomposition; LP-KSVD algorithm; classifier learning; joint kernel dictionary; locality preserving K-SVD algorithm; locality preserving term; nearest neighborhood; sparse coding; Databases; Dictionaries; Encoding; Kernel; Linear programming; Sparse matrices; Training; Discriminative Dictionary Learning; Kernel Space; Locality Preserving K-SVD;
Conference_Titel :
Multimedia and Expo (ICME), 2015 IEEE International Conference on
Conference_Location :
Turin
DOI :
10.1109/ICME.2015.7177438