133 search results - page 6 / 27 » Adaptive metric dimensionality reduction |
UM
2009
Springer
14 years 1 months ago
2009
Springer
Abstract. Words mean different things to different people, and capturing these differences is often a subtle art. These differences are often “a matter of perspective,” and...
IJCAI
2007
13 years 8 months ago
2007
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
ICPR
2002
IEEE
14 years 8 days ago
2002
IEEE
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or partbased representation of o...
ICCV
2007
IEEE
14 years 9 months ago
2007
IEEE
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
ICML
2009
IEEE
14 years 8 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...