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A fast iterative kernel PCA feature extraction for hyperspectral images

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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.
Keywords
kernel version, hyperspectral images, Feature extraction, incremental principal component analysis

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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.
@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},
}

Web of Science
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