181 search results - page 27 / 37 » Graph Embedding: A General Framework for Dimensionality Redu... |
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied ...
FSTTCS
2001
Springer
15 years 2 months ago
2001
Springer
Abstract. We propose a novel approach to clustering, based on deterministic analysis of random walks on the weighted graph associated with the clustering problem. The method is cen...
IPPS
2000
IEEE
15 years 2 months ago
2000
IEEE
Abstract. Rent's rule and related concepts of connectivity such as dimensionality, line-length distributions, and separators have found great use in fundamental studies of di ...
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JMLR
2012
13 years 4 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...
MM
2010
ACM
14 years 10 months ago
2010
ACM
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...