Scalable kernel-based learning via low-rank approximation of lifted data

Fatemeh Sheikholeslami, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Despite their well-documented capability in modeling nonlinear functions, kernel methods fall short in large-scale learning tasks due to their excess memory and computational requirements. The present work introduces a novel kernel approximation approach from a dimensionality reduction point of view on virtual lifted data. The proposed framework accommodates feature extraction while considering limited storage and computational availability, and subsequently provides kernel approximation by a linear inner-product over the extracted features. Probabilistic guarantees on the generalization of the proposed task is provided, and efficient solvers with provable convergence guarantees are developed. By introducing a sampling step which precedes the dimensionality reduction task, the framework is further broadened to accommodate learning over large datasets. The connection between the novel method and Nystrom kernel approximation algorithm with its modifications is also presented. Empirical tests validate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages596-603
Number of pages8
ISBN (Electronic)9781538632666
DOIs
StatePublished - Jan 17 2018
Event55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017 - Monticello, United States
Duration: Oct 3 2017Oct 6 2017

Publication series

Name55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
Volume2018-January

Other

Other55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
Country/TerritoryUnited States
CityMonticello
Period10/3/1710/6/17

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