Paper Info
Title
Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation
Abstract
In this contribution we consider the problem of regression estimation. We elaborate on a framework based on functional analysis giving rise to structured models in the context of reproducing kernel Hilbert spaces. In this setting the task of input selection is converted into the task of selecting functional components depending on one (or more) inputs. In turn the process of learning with embedded selection of such components can be formalized as a convex-concave problem. This results in a practical algorithm that can be implemented as a quadratically constrained quadratic programming (QCQP) optimization problem. We further investigate the mechanism of selection for the class of linear functions, establishing a relationship with LASSO.
Year
DOI
Venue
2008
10.1007/978-3-540-87536-9_19
ICANN (1)
Keywords
Field
DocType
linear function,quadratic programming,convex-concave problem,embedded selection,functional component,functional analysis,kernel regression estimation,practical algorithm,optimization problem,quadratically constrained quadratic programming,regression estimation,subspace selection,input selection,quadratically constrained quadratic program,kernel regression,reproducing kernel hilbert space
Second-order cone programming,Mathematical optimization,Quadratically constrained quadratic program,Computer science,Kernel embedding of distributions,Artificial intelligence,Quadratic programming,Variable kernel density estimation,Machine learning,Reproducing kernel Hilbert space,Kernel regression,Kernel (statistics)
Conference
Volume
ISSN
Citations 
5163
0302-9743
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Marco Signoretto11559.10
Kristiaan Pelckmans225127.44
Johan A. Suykens3984.98