37 search results - page 2 / 8 » Non-linear dimensionality reduction techniques for unsupervi... |
ICML
2008
IEEE
14 years 6 months ago
2008
IEEE
We consider feature extraction (dimensionality reduction) for compositional data, where the data vectors are constrained to be positive and constant-sum. In real-world problems, t...
SAC
2006
ACM
13 years 11 months ago
2006
ACM
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
ICANN
2003
Springer
13 years 11 months ago
2003
Springer
Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an ite...
ECCV
2004
Springer
14 years 7 months ago
2004
Springer
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classica...
SLSFS
2005
Springer
13 years 11 months ago
2005
Springer
Abstract. Mutual Information (MI) is a long studied measure of information content, and many attempts to apply it to feature extraction and stochastic coding have been made. Howeve...