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A Novel Multi-resolution Kernel Principle Component Analysis Method

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DOI: 10.23977/meimie.2019.43011

Author(s)

Jianjun Wu, Weijun Gong and Zhen Shang

Corresponding Author

Jianjun Wu

ABSTRACT

Focusing on the traditional kernel principle component analysis (KPCA) can not provide principle component information under the condition of multi-resolution, this paper proposes a novel multi-resolution kernel principle component analysis (MKPCA) method, by combining KPCA with high dimensional multi-resolution analysis theory. MKPCA can explore sample data’s characters and principle component information on the different resolution. The experiments demonstrate the feasibility of the proposed method.

KEYWORDS

Multi-resolution, principle component analysis, kernel, machine learning

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