A new method for speech signal reconstruction is proposed yb performing a nonlinear Kernel Principal Component Analysis (KPCA). By the use of kernel functions, one can efficiently compute principal components in hihg-demensional feature spaces, and reconstruct vectors mapping from input space by those dominant principal components. As the reconstructed vectors is expressed in high dimensional feature space and they could not exist pre-image in input space. For finding pre-image, we use iteration method to approximate the pre-image. The experimental results using KPCA n data reconstruction and denoising in speech signal show that it had many potnetial advantages comparing with PCA.
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