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A fast iterative kernel PCA feature extraction for hyperspectral images | IEEE Conference Publication | IEEE Xplore

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 Cand...Show More

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.
Date of Conference: 26-29 September 2010
Date Added to IEEE Xplore: 03 December 2010
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ISSN Information:

Conference Location: Hong Kong, China

References

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