Sparse regularization for semi-supervised classification

Manifold regularization (MR) is a promising regularization framework for semi-supervised learning, which introduces an additional penalty term to regularize the smoothness of functions on data manifolds and has been shown very effective in exploiting the underlying geometric structure of data for cl...

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Veröffentlicht in:	Pattern recognition 2011-08, Vol.44 (8), p.1777-1784
Hauptverfasser:	Fan, Mingyu, Gu, Nannan, Qiao, Hong, Zhang, Bo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Classification Cost function Data points Dimensionality reduction Exact sciences and technology Information, signal and communications theory Learning Least squares method Manifolds Preserves Regularization Regularization theory Regularized least square classification Semi-supervised learning Signal and communications theory Signal representation. Spectral analysis Signal, noise Telecommunications and information theory
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