A unified semi-supervised dimensionality reduction framework for manifold learning

NEUROCOMPUTING

卷:73

Article

Chatpatanasiri, Ratthachat¹  Kijsirikul, Boonserm¹ 

[1] Chulalongkorn Univ, Dept Comp Engn, Bangkok 10330, Thailand

关键词: Semi-supervised learning;    Transductive learning;    Spectral methods;    Dimensionality reduction;    Manifold learning;   

DOI  :  10.1016/j.neucom.2009.10.024

来源: Elsevier

【 摘 要 】

We present a general framework of semi-supervised dimensionality reduction for manifold learning which naturally generalizes existing supervised and unsupervised learning frameworks which apply the spectral decomposition. Algorithms derived under our framework are able to employ both labeled and unlabeled examples and are able to handle complex problems where data form separate clusters of manifolds. Our framework offers simple views, explains relationships among existing frameworks and provides further extensions which can improve existing algorithms. Furthermore, a new semi-supervised kernelization framework called KPCA trick is proposed to handle non-linear problems. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.

【 授权许可】

Free