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A kernel for multi-parameter persistent homology.

Computers & Graphics: X, (2019): 100005

Cited by: 6|Views122
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Abstract

•We propose the first kernel construction for multi-parameter persistent homology.•Our kernel is generic, stable and can be approximated in polynomial time.•Connect topological data analysis and machine learning for multivariate analysis.•Our technique is applicable to shape analysis, recognition and classification.

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