Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68344
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Title: Thresholded spectral algorithms for sparse approximations
Authors: Guo, ZC
Xiang, DH
Guo, X 
Zhou, DX
Issue Date: May-2017
Source: Analysis and applications, May 2017, v. 15, no. 3, p. 433-455
Abstract: Spectral algorithms form a general framework that unifies many regularization schemes in learning theory. In this paper, we propose and analyze a class of thresholded spectral algorithms that are designed based on empirical features. Soft thresholding is adopted to achieve sparse approximations. Our analysis shows that without sparsity assumption of the regression function, the output functions of thresholded spectral algorithms are represented by empirical features with satisfactory sparsity, and the convergence rates are comparable to those of the classical spectral algorithms in the literature.
Keywords: Learning theory
Thresholded spectral algorithm
Sparsity
Learning rate
Publisher: World Scientific
Journal: Analysis and applications 
ISSN: 0219-5305
EISSN: 1793-6861
DOI: 10.1142/S0219530517500026
Rights: Electronic version of an article published as Analysis and Applications, vol. 15, no. 3, 2017, p. 433-455, https://doi.org/10.1142/S0219530517500026, © World Scientific Publishing Company, https://www.worldscientific.com/worldscinet/aa
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