70 search results - page 6 / 14 » Multiple Kernel Learning for Dimensionality Reduction |
JMLR
2012
11 years 11 months ago
2012
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
CORR
2007
Springer
13 years 8 months ago
2007
Springer
We consider the least-square regression problem with regularization by a block 1-norm, that is, a sum of Euclidean norms over spaces of dimensions larger than one. This problem, r...
ICML
2004
IEEE
14 years 9 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
2003
IEEE
14 years 9 months ago
2003
IEEE
A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a ...
BMCBI
2010
13 years 8 months ago
2010
Background: Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduc...