A fast automatic low-rank determination algorithm for noisy matrix completion

Tatsuya Yokota, Andrzej Cichocki

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

5 Citations (Scopus)

Abstract

Rank estimation is an important factor for low-rank based matrix completion, and most works devoted to this problem have considered the minimization of nuclear norm instead of matrix rank. However, when nuclear norm minimization shifts to 'regularization' due to noise, it is difficult to estimate original matrix rank, precisely. In present paper, we propose a new fast algorithm to precisely estimate matrix rank and perform completion without using nuclear norm. In our extensive experiments, the proposed algorithm significantly outperformed nuclear-norm based method for accuracy, especially and Incremental OptSpace regarding computational time. Our model selection scheme has many promising extensions for constrained matrix factorizations and tensor decompositions, and these extensions could be useful for wide range of practical applications.

Original languageEnglish
Title of host publication2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages43-46
Number of pages4
ISBN (Electronic)9789881476807
DOIs
Publication statusPublished - 19 Feb 2016
Externally publishedYes
Event2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
Duration: 16 Dec 201519 Dec 2015

Publication series

Name2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

Conference

Conference2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Country/TerritoryHong Kong
CityHong Kong
Period16/12/1519/12/15

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