DMCA
A General Framework for Manifold Alignment
Cached
Download Links
| Citations: | 12 - 2 self |
BibTeX
@MISC{Wang_ageneral,
author = {Chang Wang and Sridhar Mahadevan},
title = {A General Framework for Manifold Alignment},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Manifold alignment has been found to be useful in many fields of machine learning and data mining. In this paper we summarize our work in this area and introduce a general framework for manifold alignment. This framework generates a family of approaches to align manifolds by simultaneously matching the corresponding instances and preserving the local geometry of each given manifold. Some approaches like semi-supervised alignment and manifold projections can be obtained as special cases. Our framework can also solve multiple manifold alignment problems and be adapted to handle the situation when no correspondence information is available. The approaches are described and evaluated both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our methods including identification of topics shared by multiple document collections, and biological structure alignment are discussed in the paper.
Keyphrases
manifold alignment general framework data mining multiple document collection local geometry correspondence information semi-supervised alignment machine learning multiple manifold alignment problem novel application many field manifold projection useful knowledge transfer special case corresponding instance biological structure alignment
