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 MoreMetadata
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.
Published in: 2010 IEEE International Conference on Image Processing
Date of Conference: 26-29 September 2010
Date Added to IEEE Xplore: 03 December 2010
ISBN Information: