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- Steinert, RebeccaKTH,Numerisk Analys och Datalogi, NADA,Beräkningsbiologi, CB (author)
Recognition of handwritten digits using sparse codes generated by local feature extraction methods
- Article/chapterEnglish2006
Publisher, publication year, extent ...
- 2006
- printrdacarrier
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- LIBRIS-ID:oai:DiVA.org:kth-6305
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-6305URI
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- Language:English
- Summary in:English
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- QC 20100916
- We investigate when sparse coding of sensory inputs canimprove performance in a classification task. For this purpose, we use astandard data set, the MNIST database of handwritten digits. We systematicallystudy combinations of sparse coding methods and neural classifiersin a two-layer network. We find that processing the image data intoa sparse code can indeed improve the classification performance, comparedto directly classifying the images. Further, increasing the level of sparsenessleads to even better performance, up to a point where the reductionof redundancy in the codes is offset by loss of information.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- Computer science
- Datalogi
Added entries (persons, corporate bodies, meetings, titles ...)
- Rehn, MartinKTH,Numerisk Analys och Datalogi, NADA(Swepub:kth)u1n8grvo (author)
- Lansner, AndersKTH,Numerisk Analys och Datalogi, NADA(Swepub:kth)u12s8cr8 (author)
- KTHNumerisk Analys och Datalogi, NADA (creator_code:org_t)
Related titles
- In:ESANN'2006, s. 161-1662930307064
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