NDLI: Learning smooth models of nonsmooth functions via convex optimization

Content Provider	IEEE Xplore Digital Library
Author	Lauer, F. Le, V.L. Bloch, G.
Copyright Year	2012
Description	Author affiliation: Université de Lorraine, LORIA, UMR 7503, CNRS, Inria, France (Lauer, F.) \|\| Université de Lorraine, CRAN, UMR 7039, CNRS, France (Le, V.L.; Bloch, G.)
Abstract	This paper proposes a learning framework and a set of algorithms for nonsmooth regression, i.e., for learning piecewise smooth target functions with discontinuities in the function itself or the derivatives at unknown locations. In the proposed approach, the model belongs to a class of smooth functions. Though constrained to be globally smooth, the trained model can have very large derivatives at particular locations to approximate the nonsmoothness of the target function. This is obtained through the definition of new regularization terms which penalize the derivatives in a location-dependent manner and training algorithms in the form of convex optimization problems. Examples of application to hybrid dynamical system identification and image reconstruction are provided.
Starting Page	1
Ending Page	6
File Size	576070
Page Count	6
File Format	PDF
ISBN	9781467310246
ISSN	15512541
e-ISBN	9781467310260
e-ISBN	9781467310253
DOI	10.1109/MLSP.2012.6349755
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Training Vectors Optimization Machine learning Convex functions Shape
Content Type	Text
Resource Type	Article

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