Abstract
This paper determined weights of the mixture kernel of KPCA using the similarity of dataset original and kernel matrix from sample and kernel principal contribution adaptive. It provides an effective way for determining the parameter of the mixture of kernel of function of applying KPCA to multi-index evaluation method. We believe that with its advantages embodied in specific issues, it will be more widely applied to feature extraction and make the evaluation method more scientific and effective.
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Wan, X., Guo, J., Tan, Y. (2011). The Similarity Weighting Method of Mixtures Kernel in the Synthetic Evalution Function of KPCA. In: Liu, C., Chang, J., Yang, A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27452-7_77
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DOI: https://doi.org/10.1007/978-3-642-27452-7_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27451-0
Online ISBN: 978-3-642-27452-7
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