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A fast iterative kernel PCA feature extraction for hyperspectral images

Wenzhi Liao UGent, Aleksandra Pizurica UGent, Wilfried Philips UGent and Youguo Pi (2010) IEEE International Conference on Image Processing ICIP. p.1317-1320
abstract
A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyperspectral images. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which solves the eigenvectors through iteration. Without performing eigen decomposition on Gram matrix, our method can reduce the space complexity and time complexity greatly. Experimental results were validated in comparison with the standard KPCA and linear version methods.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
kernel version, hyperspectral images, Feature extraction, incremental principal component analysis
in
IEEE International Conference on Image Processing ICIP
IEEE Int. Conf. Image Proc.
editor
Bonnie Law
issue title
2010 IEEE international conference on image processing
pages
1317 - 1320
publisher
IEEE
place of publication
New York, NY, USA
conference name
2010 IEEE 17th International conference on Image Processing (ICIP 2010)
conference location
Hong Kong, PR China
conference start
2010-09-26
conference end
2010-09-30
Web of Science type
Proceedings Paper
Web of Science id
000287728001104
ISSN
1522-4880
ISBN
9781424479931
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1058464
handle
http://hdl.handle.net/1854/LU-1058464
date created
2010-10-13 16:27:19
date last changed
2017-01-02 09:52:09
@inproceedings{1058464,
  abstract     = {A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyperspectral images. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which solves the eigenvectors through iteration. Without performing eigen decomposition on Gram matrix, our method can reduce the space complexity and time complexity greatly. Experimental results were validated in comparison with the standard KPCA and linear version methods.},
  author       = {Liao, Wenzhi and Pizurica, Aleksandra and Philips, Wilfried and Pi, Youguo},
  booktitle    = {IEEE International Conference on Image Processing ICIP},
  editor       = {Law, Bonnie},
  isbn         = {9781424479931},
  issn         = {1522-4880},
  keyword      = {kernel version,hyperspectral images,Feature extraction,incremental principal component analysis},
  language     = {eng},
  location     = {Hong Kong, PR China},
  pages        = {1317--1320},
  publisher    = {IEEE},
  title        = {A fast iterative kernel PCA feature extraction for hyperspectral images},
  year         = {2010},
}

Chicago
Liao, Wenzhi, Aleksandra Pizurica, Wilfried Philips, and Youguo Pi. 2010. “A Fast Iterative Kernel PCA Feature Extraction for Hyperspectral Images.” In IEEE International Conference on Image Processing ICIP, ed. Bonnie Law, 1317–1320. New York, NY, USA: IEEE.
APA
Liao, Wenzhi, Pizurica, A., Philips, W., & Pi, Y. (2010). A fast iterative kernel PCA feature extraction for hyperspectral images. In B. Law (Ed.), IEEE International Conference on Image Processing ICIP (pp. 1317–1320). Presented at the 2010 IEEE 17th International conference on Image Processing (ICIP 2010), New York, NY, USA: IEEE.
Vancouver
1.
Liao W, Pizurica A, Philips W, Pi Y. A fast iterative kernel PCA feature extraction for hyperspectral images. In: Law B, editor. IEEE International Conference on Image Processing ICIP. New York, NY, USA: IEEE; 2010. p. 1317–20.
MLA
Liao, Wenzhi, Aleksandra Pizurica, Wilfried Philips, et al. “A Fast Iterative Kernel PCA Feature Extraction for Hyperspectral Images.” IEEE International Conference on Image Processing ICIP. Ed. Bonnie Law. New York, NY, USA: IEEE, 2010. 1317–1320. Print.