Kernel Geometric Mean Metric Learning

Zixin Feng; Teligeng Yun; Yu Zhou; Ruirui Zheng; Jianjun He

doi:10.20944/preprints202307.1650.v2

Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 24 July 2023 / Approved: 24 July 2023 / Online: 25 July 2023 (07:32:40 CEST)
Version 2 : Received: 29 July 2023 / Approved: 31 July 2023 / Online: 1 August 2023 (11:46:59 CEST)

This paper propose a kernel geometric mean metric learning (KGMML) algorithm. The basic idea is to obtain the closed-form solution of the geometric mean metric learning (GMML) algorithm in the high-dimensional feature space determined by the kernel function. Then, the solution is generalized as a form of kernel matrix by using the integral representation of the weighted geometric mean and the Woodbury matrix in this new feature space. Experimental results on 15 datasets show that the proposed algorithm can effectively improve the accuracy of the GMML algorithm and other metric algorithms.

metric learning; kernel methods; weighted geometric mean

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Comment 1

Received: 1 August 2023
Commenter: Zixin Feng
Commenter's Conflict of Interests: Author

Comment: Changing the author's order of precedence.

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Kernel Geometric Mean Metric Learning

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