A unified semi-supervised dimensionality reduction framework for manifold learning

Authors:
Ratthachat Chatpatanasiri;Boonserm Kijsirikul
Affiliations:
Department of Computer Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand;Department of Computer Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand
Venue:
Neurocomputing
Year:
2010

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Abstract

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.