Multiple Kernel Sparse Representation Based Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Hao Zheng , Fan Liu , Zhong Jin

ABSTRACT

Sparse representation based classification (SRC) has been very successful in many pattern recognition problems. Recently, some extended kernel methods have been proposed through mapping the samples from original feature space into a high dimensional feature space, and then performing the SRC in the high dimensional feature space. However they are all simple kernel methods whose kernel is not most suitable one. For addressing this question, we proposed a novel method named multiple kernel sparse representation based classification (MKSRC), which combine several possible kernels and make full of kernel information. More importantly kernel weights of MKSRC can be automatically selected. The experimental results of face databases indicated recognition performance of new method is superior to other state-of-the-art methods. More... »

PAGES

48-55

References to SciGraph publications

Book

TITLE

Pattern Recognition

ISBN

978-3-642-33505-1
978-3-642-33506-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-33506-8_7

DOI

http://dx.doi.org/10.1007/978-3-642-33506-8_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005593169


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