We introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters\r\nrequired in the calculation of a kernel matrix. This new methodology incorporatesmultiscale methods into KPCA for transforming\r\nmultiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in\r\nKPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered\r\nsimulation data. We show that WKPCA is an effective feature extraction method for transforming a variety of multidimensional\r\nclustered data into data with a higher level of linearity among the data attributes. That brings an improvement in the accuracy of\r\nsimple linear classifiers. Based on the analysis of the simulation data sets, we observe that multiscale translation invariant wavelet\r\nkernels for KPCA has an enhanced performance in feature extraction. The application of the proposed method to real data is also\r\naddressed.
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