3006 search results - page 70 / 602 » Dimensionality reduction and generalization |
ICML
2007
IEEE
14 years 10 months ago
2007
IEEE
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essen...
ICML
2005
IEEE
14 years 10 months ago
2005
IEEE
Dimensionality reduction is the problem of finding a low-dimensional representation of highdimensional input data. This paper examines the case where additional information is kno...
WACV
2002
IEEE
14 years 2 months ago
2002
IEEE
In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Co...
NIPS
2004
13 years 11 months ago
2004
We describe a way of using multiple different types of similarity relationship to learn a low-dimensional embedding of a dataset. Our method chooses different, possibly overlappin...
JMLR
2012
12 years 11 days 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...