166 search results - page 5 / 34 » Learning a kernel matrix for nonlinear dimensionality reduct... |
KDD
2007
ACM
14 years 6 months ago
2007
ACM
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
ICML
2006
IEEE
14 years 6 months ago
2006
IEEE
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior information is available, namely, semi-supervised dimensionality reduction. It is...