Tighter generalization bounds for matrix completion via factorization into constrained matrices

Ken ichiro Moridomi, Kohei Hatano, Eiji Takimoto

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

We prove generalization error bounds of classes of low-rank matrices with some norm constraints for collaborative filtering tasks. Our bounds are tighter, compared to known bounds using rank or the related quantity only, by taking the additional L1 and L constraints into account. Also, we show that our bounds on the Rademacher complexity of the classes are optimal.

Original languageEnglish
Pages (from-to)1997-2004
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE101D
Issue number8
DOIs
Publication statusPublished - Aug 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Tighter generalization bounds for matrix completion via factorization into constrained matrices'. Together they form a unique fingerprint.

Cite this