Manifold Learning via Multi-Penalty Regularization
نویسندگان
چکیده
منابع مشابه
Manifold learning via Multi-Penalty Regularization
Manifold regularization is an approach which exploits the geometry of the marginal distribution. The main goal of this paper is to analyze the convergence issues of such regularization algorithms in learning theory. We propose a more general multi-penalty framework and establish the optimal convergence rates under the general smoothness assumption. We study a theoretical analysis of the perform...
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ژورنال
عنوان ژورنال: International Journal of Artificial Intelligence & Applications
سال: 2017
ISSN: 0976-2191,0975-900X
DOI: 10.5121/ijaia.2017.8506