A fast iterative kernel PCA feature extraction for hyperspectral images

Wenzhi Liao, Aleksandra Pizurica, Wilfried Philips, Youguo Pi, Bonnie Law (Editor)

Research output: Contribution to conferencePaper

13 Citations (Scopus)

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.
Original languageEnglish
Pages1317-1320
Number of pages4
DOIs
Publication statusPublished - 3 Dec 2010
Event2010 IEEE 17th International conference on Image Processing (ICIP 2010) - Hong Kong, China
Duration: 26 Sep 201030 Sep 2010

Conference

Conference2010 IEEE 17th International conference on Image Processing (ICIP 2010)
Abbreviated titleICIP 2010
CountryChina
CityHong Kong
Period26/09/1030/09/10

Keywords

  • kernel version
  • hyperspectral images
  • feature extraction
  • incremental principal component analysis
  • complexity theory
  • computational complexity
  • eigenvalues and eigenfunctions
  • geophysical image processing

Cite this

Liao, W., Pizurica, A., Philips, W., Pi, Y., & Law, B. (Ed.) (2010). A fast iterative kernel PCA feature extraction for hyperspectral images. 1317-1320. Paper presented at 2010 IEEE 17th International conference on Image Processing (ICIP 2010), Hong Kong, China. https://doi.org/10.1109/ICIP.2010.5651670