Article
Version 1
Preserved in Portico This version is not peer-reviewed
Kernel Geometric Mean Metric Learning
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)
Version 2 : Received: 29 July 2023 / Approved: 31 July 2023 / Online: 1 August 2023 (11:46:59 CEST)
A peer-reviewed article of this Preprint also exists.
Feng, Z.; Yun, T.; Zhou, Y.; Zheng, R.; He, J. Kernel Geometric Mean Metric Learning. Appl. Sci. 2023, 13, 12047. Feng, Z.; Yun, T.; Zhou, Y.; Zheng, R.; He, J. Kernel Geometric Mean Metric Learning. Appl. Sci. 2023, 13, 12047.
Abstract
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.
Keywords
Metric learning; Kernel methods; Weighted geometric mean
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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