Unsupervised learning by convex and conic coding

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

83 Scopus citations

Abstract

Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or conic combination of basis vectors to the input. The learning algorithms produce basis vectors that minimize the reconstruction error of the convex algorithm develops locally linear models of the input, while the conic algorithm discovers features. Both algorithms are used to model handwritten digits and compared with vector quantization and principal component analysis. The neural network implementations involve feedback connections that project a reconstruction back to the input layer.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996
PublisherNeural information processing systems foundation
Pages515-521
Number of pages7
ISBN (Print)0262100657, 9780262100656
StatePublished - Jan 1 1997
Event10th Annual Conference on Neural Information Processing Systems, NIPS 1996 - Denver, CO, United States
Duration: Dec 2 1996Dec 5 1996

Publication series

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

Other

Other10th Annual Conference on Neural Information Processing Systems, NIPS 1996
CountryUnited States
CityDenver, CO
Period12/2/9612/5/96

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Unsupervised learning by convex and conic coding'. Together they form a unique fingerprint.

Cite this