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This paper confirms the suitability of kernel principal component analysis (KPCA) as a robust feature extraction and denoising method in sensor array based vapor detection system (E-nose). Particularly the study focuses on response analysis of surface acoustic wave (SAW) sensor array in chemical class recognition of volatile organic compounds (VOCs). Usually KPCA results deprived performance compare...
In this paper, we propose a novel speech feature extraction method using kernel principal component analysis (KPCA) based on kernel fuzzy K-means clustering. First, all frames of speech signal are divided into a given amount of clusters by kernel-based fuzzy K-means clustering and then features are extracted by KPCA, as a result of which the storage and computational complexity can be reduced and...
Kernel Principal Component Analysis (KPCA) is a widely used technique in the dimension reduction, de-noising and discovering nonlinear intrinsic dimensions of data set. In this paper we describe a reweighing kernel-based classification method for improving recognition problem. Firstly, we map the training samples to the feature space by non-linear transformation, and then perform principal component...
After the full understanding of principal component analysis and kernel principal component analysis, the validity of kernel principal component analysis method is proved, and a new grid-based parameter optimization algorithm is introduced. Combining with BPNN, the classification experimental results show that this parameter optimization algorithm can get good results, The performance is superior...
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