148 search results - page 3 / 30 » DiscLDA: Discriminative Learning for Dimensionality Reductio... |
ML
2010
ACM
13 years 3 months ago
2010
ACM
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...
CVPR
2008
IEEE
13 years 5 months ago
2008
IEEE
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in...
ICDM
2003
IEEE
13 years 10 months ago
2003
IEEE
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and com...
ICML
2007
IEEE
14 years 6 months ago
2007
IEEE
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. LDA in the binaryclass case has been shown to be equivalent to linear re...
CVPR
2008
IEEE
14 years 7 months ago
2008
IEEE
Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...