Mathematical Analysis on Information-Theoretic Metric Learning With Application to Supervised Learning

被引：0

作者：

Choi, Jooyeon ^{[1
]}

Min, Chohong ^{[1
]}

Lee, Byungjoon ^{[2
]}

机构：

[1] Ewha Womans Univ, Dept Math, Seoul 03760, South Korea

[2] Catholic Univ Korea, Dept Math, Bucheon 14662, South Korea

来源：

IEEE ACCESS | 2019年 / 7卷

基金：

新加坡国家研究基金会;

关键词：

Bregman iteration; machine learning algorithm; mathematical analysis; metric learning; convex optimization; PERSON REIDENTIFICATION;

D O I：

10.1109/ACCESS.2019.2937973

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article presents a concrete mathematical analysis on Information-Theoretic Metric Learning (ITML). The analysis provides a theoretical foundation for ITML, by supplying well-posedness, strong duality, and convergence. Our analysis suggests the correction of a typo in the original ITML article that may lead to the loss of accuracy in the metric learning. The necessity of this correction is confirmed by several numerical experiments on supervised learning.

引用

页码：121998 / 122005

页数：8

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