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Register a new userA Unified Semi-Supervised Dimensionality Reduction Framework for Manifold Learning
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Ratthachat Chatpatanasiri
Publication date
2009
fields
Informatics Engineering
and research's language is
English
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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.
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