Sparse multi-prototype classification

Vikas K. Garg, Lin Xiao, Ofer Dekel

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

We introduce a new class of sparse multi-prototype classifiers, designed to combine the computational advantages of sparse predictors with the non-linear power of prototype-based classification techniques. This combination makes sparse multi-prototype models especially well-suited for resource constrained computational platforms, such as the IoT devices. We cast our supervised learning problem as a convex-concave saddle point problem and design a provably-fast algorithm to solve it. We complement our theoretical analysis with an empirical study that demonstrates the merits of our methodology.

Original languageEnglish
Title of host publication34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
EditorsRicardo Silva, Amir Globerson, Amir Globerson
Pages704-714
Number of pages11
ISBN (Electronic)9781510871601
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventConference on Uncertainty in Artificial Intelligence - Monterey, United States
Duration: 6 Aug 201810 Aug 2018

Conference

ConferenceConference on Uncertainty in Artificial Intelligence
Abbreviated titleUAI
CountryUnited States
CityMonterey
Period06/08/201810/08/2018

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