Sparse dictionary learning from 1-BIT data

Jarvis D Haupt, Nikolaos Sidiropoulos, Georgios B Giannakis

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

5 Scopus citations

Abstract

This work examines a sparse dictionary learning task - that of fitting a collection of data points, arranged as columns of a matrix, to a union of low-dimensional linear subspaces - in settings where only highly quantized (single bit) observations of the data matrix entries are available. We analyze a complexity penalized maximum likelihood estimation strategy, and obtain finite-sample bounds for the average per-element squared approximation error of the estimate produced by our approach. Our results are reminiscent of traditional parametric estimation tasks - we show here that despite the highly-quantized observations, the normalized per-element estimation error is bounded by the ratio between the number of 'degrees of freedom' of the matrix and its dimension.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7664-7668
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • Sparse dictionary learning
  • complexity regularization
  • maximum likelihood estimation

Fingerprint Dive into the research topics of 'Sparse dictionary learning from 1-BIT data'. Together they form a unique fingerprint.

Cite this