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Li et al., 2012 - Google Patents

Sparse data-dependent kernel principal component analysis based on least squares support vector machine for feature extraction and recognition

Li et al., 2012

Document ID
15017780974639382257
Author
Li J
Gao H
Publication year
Publication venue
Neural Computing and Applications

External Links

Snippet

Kernel learning is widely used in many areas, and many methods are developed. As a famous kernel learning method, kernel principal component analysis (KPCA) endures two problems in the practical applications. One is that all training samples need to be stored for …
Continue reading at link.springer.com (other versions)

Classifications

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