A fast iterative kernel PCA feature extraction for hyperspectral images

Wenzhi Liao; Aleksandra Pizurica; Wilfried Philips; Youguo Pi; Bonnie Law

doi:10.1109/ICIP.2010.5651670

A fast iterative kernel PCA feature extraction for hyperspectral images

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

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

24 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 language	English
Pages	1317-1320
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5651670
Publication status	Published - 3 Dec 2010
Event	2010 IEEE 17th International conference on Image Processing (ICIP 2010) - Hong Kong, China Duration: 26 Sept 2010 → 30 Sept 2010

Conference

Conference	2010 IEEE 17th International conference on Image Processing (ICIP 2010)
Abbreviated title	ICIP 2010
Country/Territory	China
City	Hong Kong
Period	26/09/10 → 30/09/10

Keywords

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

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10.1109/ICIP.2010.5651670

Cite this

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title = "A fast iterative kernel PCA feature extraction for hyperspectral images",

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, complexity theory, computational complexity, eigenvalues and eigenfunctions, geophysical image processing",

author = "Wenzhi Liao and Aleksandra Pizurica and Wilfried Philips and Youguo Pi and Bonnie Law",

year = "2010",

month = dec,

day = "3",

doi = "10.1109/ICIP.2010.5651670",

language = "English",

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TY - CONF

T1 - A fast iterative kernel PCA feature extraction for hyperspectral images

AU - Liao, Wenzhi

AU - Pizurica, Aleksandra

AU - Philips, Wilfried

AU - Pi, Youguo

A2 - Law, Bonnie

PY - 2010/12/3

Y1 - 2010/12/3

N2 - 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.

AB - 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.

KW - kernel version

KW - hyperspectral images

KW - feature extraction

KW - incremental principal component analysis

KW - complexity theory

KW - computational complexity

KW - eigenvalues and eigenfunctions

KW - geophysical image processing

UR - http://hdl.handle.net/1854/LU-1058464

U2 - 10.1109/ICIP.2010.5651670

DO - 10.1109/ICIP.2010.5651670

M3 - Paper

SP - 1317

EP - 1320

T2 - 2010 IEEE 17th International conference on Image Processing (ICIP 2010)

Y2 - 26 September 2010 through 30 September 2010

ER -

A fast iterative kernel PCA feature extraction for hyperspectral images

Abstract

Conference

Keywords

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