Features as sufficient statistics

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

Abstract

An image is often represented by a set of detected features. We get an enormous compression by representing images in this way. Furthermore, we get a representation which is little affected by small amounts of noise in the image. However, features are typically chosen in an ad hoc manner. We show how a good set of features can be obtained using sufficient statistics. The idea of sparse data representation naturally arises. We treat the 1-dimensional and 2-dimensional signal reconstruction problem to make our ideas concrete.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PublisherNeural information processing systems foundation
Pages794-800
Number of pages7
ISBN (Print)0262100762, 9780262100762
StatePublished - Jan 1 1998
Event11th Annual Conference on Neural Information Processing Systems, NIPS 1997 - Denver, CO, United States
Duration: Dec 1 1997Dec 6 1997

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other11th Annual Conference on Neural Information Processing Systems, NIPS 1997
CountryUnited States
CityDenver, CO
Period12/1/9712/6/97

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Geiger, D., Rudra, A., & Maloney, L. (1998). Features as sufficient statistics. In Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997 (pp. 794-800). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.