NDLI: Gradient Hyper-parameter Optimization for Manifold Regularization

Content Provider	IEEE Xplore Digital Library
Author	Becker, C.O. Ferreira, P.A.V.
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. & Comput. Eng., State Univ. of Campinas (Unicamp), Campinas, Brazil (Becker, C.O.; Ferreira, P.A.V.)
Abstract	Semi-supervised learning can be defined as the ability to improve the predictive performance of an algorithm by providing it with data which hasn't been previously labeled. Manifold Regularization is a semi-supervised learning approach that extends the regularization framework so as to include additional regularization penalties that are based on the graph Laplacian as the empirical estimator of the underlying manifold. The incorporation of such terms rely on additional hyper-parameters, which, together with the original kernel and regularization parameters, are known to influence algorithm behavior. This paper proposes a gradient approach to the optimization of such hyper-parameters which is based on the closed form for the generalized cross validation estimate, being valid when the learning optimality conditions can be represented as a linear system, such as is the case for Laplacian Regularized Least Squares. For the subset hyper-parameters that are integer quantities, as is the case for the Laplacian matrix hyper-parameters, we propose the optimization of the weight components of a sum of base terms. Results of computational experiments are presented to illustrate the technique proposed.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	339
Ending Page	344
File Size	421467
Page Count	6
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Training Laplace equations Semi-supervised Learning Error analysis Manifold Regularization Model Selection Hyper-parameter Optimization Vectors Kernel Machine Learning Optimization
Content Type	Text
Resource Type	Article

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