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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7184 |
| Date | 01 December 1996 |
| Creators | Olshausen, Bruno A. |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 5 p., 233466 bytes, 268006 bytes, application/postscript, application/pdf |
| Relation | AIM-1580, CBCL-138 |
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