166 search results - page 4 / 34 » Learning a kernel matrix for nonlinear dimensionality reduct... |
AUSAI
2006
Springer
13 years 9 months ago
2006
Springer
In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data...
ICML
2007
IEEE
14 years 6 months ago
2007
IEEE
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spect...
JMLR
2012
11 years 7 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...
ML
2006
ACM
13 years 5 months ago
2006
ACM
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construc...
WEBI
2010
Springer
13 years 3 months ago
2010
Springer
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...