Extending information maximization from a rate-distortion perspective

Yan Zhang, Junjie Hu, Takayuki Okatani

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a new interpretation of the information maximization method (InfoMax) from a perspective of the rate distortion theory. We show that under specific conditions, InfoMax is equivalent to the minimization of a compression rate under the constraint of zero distortion. Zero distortion, or equivalently, zero reconstruction error between the input and its reconstruction, does not provide meaningful solutions in many cases. Based on the new interpretation, we extend InfoMax to be able to deal with non-zero distortion and also to learn under/over-complete representations. Experimental results on synthetic as well as real data show the effectiveness of our method.

Original languageEnglish
Pages (from-to)285-295
Number of pages11
JournalNeurocomputing
Volume399
DOIs
Publication statusPublished - 2020 Jul 25

Keywords

  • Information maximization
  • Rate distortion
  • Unsupervised representation learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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