.png){kind=icon}
.png){kind=icon}
Return
Uncorrelated discriminant vectors using a kernel method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2004.06.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Uncorrelated discriminant vectors using a kernel method are proposed in this paper. In some sense, kernel uncorrelated discriminant vectors extend Jin's method and then several related theorems are stated. Most importantly, the proposed method can deal with nonlinear problems. Finally, experimental results on handwritten numeral characters show that the proposed method is effective and feasible. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
linear discriminant analysis
kernel-based methods
uncorrelated discriminant vectors
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
No organization information available