Linear discriminant analysis (LDA) is a simple but widely used algorithm in the area of pattern recognition. However, it has some shortcomings in that it is sensitive to outliers and limited to linearly separable cases. To solve these problems, in this paper, a non-linear robust variant of LDA, call
โฆ LIBER โฆ
Fuzzy discriminant analysis with kernel methods
โ Scribed by Xiao-Hong Wu; Jian-Jiang Zhou
- Book ID
- 108234382
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 199 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0031-3203
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
Robust kernel discriminant analysis usin
โ
Gyeongyong Heo; Paul Gader
๐
Article
๐
2011
๐
Elsevier Science
๐
English
โ 472 KB
An efficient kernel discriminant analysi
โ
Juwei Lu; K.N. Plataniotis; A.N. Venetsanopoulos; Jie Wang
๐
Article
๐
2005
๐
Elsevier Science
๐
English
โ 167 KB
Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, w
Speed up kernel discriminant analysis
โ
Deng Cai; Xiaofei He; Jiawei Han
๐
Article
๐
2010
๐
Springer-Verlag
๐
English
โ 356 KB
Kernel clustering-based discriminant ana
โ
Bo Ma; Hui-yang Qu; Hau-san Wong
๐
Article
๐
2007
๐
Elsevier Science
๐
English
โ 168 KB
Parametric and kernel density methods in
โ
B.J. Murphy; M.A. Moran
๐
Article
๐
1986
๐
Elsevier Science
๐
English
โ 544 KB
Fuzzy discriminant analysis in fuzzy gro
โ
Junzo Watada; Hideo Tanaka; Kiyoji Asai
๐
Article
๐
1986
๐
Elsevier Science
๐
English
โ 390 KB