Learning Linear, Sparse, Factorial Codes

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7184

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DC Field	Value	Language
dc.creator	Olshausen, Bruno A.	-
dc.date	2004-10-20T20:49:08Z	-
dc.date	2004-10-20T20:49:08Z	-
dc.date	1996-12-01	-
dc.date.accessioned	2013-10-09T02:48:29Z	-
dc.date.available	2013-10-09T02:48:29Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1580	-
dc.identifier	CBCL-138	-
dc.identifier	http://hdl.handle.net/1721.1/7184	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	In previous work (Olshausen & Field 1996), 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 algorithm of Bell and Sejnowski (1995), and it suggests how to adapt parameters that were previously fixed.	-
dc.format	5 p.	-
dc.format	233466 bytes	-
dc.format	268006 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1580	-
dc.relation	CBCL-138	-
dc.subject	unsupervised learning	-
dc.subject	factorial coding	-
dc.subject	sparse coding	-
dc.subject	MIT	-
dc.title	Learning Linear, Sparse, Factorial Codes	-
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