.png){kind=icon}
.png){kind=icon}
Return
On applying linear discriminant analysis for multi-labeled problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.patrec.2008.01.003.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, but it is originally focused on a single-abeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective in a high dimensional multi-labeled problem. Experimental results demonstrate that by considering multi-labeled structure, LDA can achieve computational efficiency and also improve classification performances. (C) 2008 Elsevier B.V. All rights reserved.
Keywords:
dimension reduction
linear discriminant analysis
multi-labeled problems
text categorization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.9K
Citations:
1.6W