.png){kind=icon}
返回
Discriminative subspace learning using generalized mean
.png){kind=icon}
OA
AI
DOI：10.1016/j.sigpro.2024.109421.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Linear discriminant analysis (LDA) is one of the most popular methods to extract discriminative features because it is simple and powerful. However, LDA fails to learn a discriminative subspace in some cases. This study deals with a problem of LDA, the so-called class separation (CS) problem, which means that some classes located close to each other in the original input space tend to overlap in a learned subspace. This problem can also happen in a heteroscedastic extension of LDA, the oriented discriminant analysis (ODA). To alleviate the problem, we propose two methods to maximize the generalized mean instead of the arithmetic mean in the objective functions. Experimental results show that the proposed methods can obtain better discriminative subspaces than LDA, ODA, and other alternatives designed to solve the CS problem.
Keyword:
Generalized mean
Class separation problem
Linear discriminant analysis
Feature extraction
Dimensionality reduction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
引用论文
Max-min distance analysis by making a uniform distribution of class centers for dimensionality reduction.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏分享收藏
查看更多内容