TR: sparse coding and ICA

[Bruno A. Olshausen]

The following TR is available via 
ftp://publications.ai.mit.edu/ai-publications/1500-1999/AIM-1580.ps.Z


	       Learning Linear, Sparse, Factorial Codes

			Bruno A. Olshausen

  In previous work (Nature, 381:607-609), an algorithm was described
  for learning linear sparse codes which, when trained on natural
  images, produces a set of basis functions that are spatially
  localized, oriented, and bandpass (i.e., wavelet-like).  This note
  shows how the algorithm may be interpreted within a maximum-likelihood
  framework.  Several useful insights emerge from this connection: it
  makes explicit the relation to statistical independence (i.e.,
  factorial coding), it shows a formal relationship to the "independent
  components analysis" algorithm of Bell and Sejnowski (1995), and it
  suggests how to adapt parameters that were previously fixed.


Related papers are available via http://redwood.ucdavis.edu/bruno/papers.html

Bruno A. Olshausen			Phone:	(916) 757-8749
Center for Neuroscience			Fax:	(916) 757-8827
UC Davis				Email:	baolshausen at ucdavis.edu
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Davis, CA 95616