http://iet.metastore.ingenta.com
1887

Producing computationally efficient KPCA-based feature extraction for classification problems

Producing computationally efficient KPCA-based feature extraction for classification problems

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

An improvement to kernel principal component analysis (KPCA) to produce computationally efficient KPCA-based feature extraction is proposed. This improvement is applicable to all cases no matter whether the samples in the feature space have zero mean or not. Experiments on several benchmark datasets show that the improvement performs well in classification problems.

References

    1. 1)
      • Y. Xu , D. Zhang , F. Song , J.-Y. Yang , Z. Jing , M. Li . A method for speeding up feature extraction based on KPCA. Neurocomputing , 1056 - 1061
    2. 2)
      • J. Yang , Z. Jin , J.-Y. Yang , D. Zhang . Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recognit. , 2097 - 2100
    3. 3)
      • Y. Xu , J.-Y. Yang , J. Lu , D.-J. Yu . An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments. Pattern Recognit. , 2091 - 2094
    4. 4)
      • Y. Xu , J.-Y. Yang , J. Yang . A reformative kernel Fisher discriminant analysis. Pattern Recognit. , 1299 - 1302
    5. 5)
      • Twining, C., Taylor, C.: `Kernel principal component analysis and the construction of non-linear active shape models', British Machine Vision Conf., 2001, Manchester, UK, 1, p. 23–32.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2010.2814
Loading

Related content

content/journals/10.1049/el.2010.2814
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address