In:
Neural Computation, MIT Press, Vol. 19, No. 7 ( 2007-07), p. 1897-1918
Abstract:
Sparse coding is an important approach for the unsupervised learning of sensory features. In this contribution, we present two new methods that extend the traditional sparse coding approach with supervised components. Our goal is to increase the suitability of the learned features for classification tasks while keeping most of their general representation capability. We analyze the effect of the new methods using visualization on artificial data and discuss the results on two object test sets with regard to the properties of the found feature representation.
Type of Medium:
Online Resource
ISSN:
0899-7667
,
1530-888X
DOI:
10.1162/neco.2007.19.7.1897
Language:
English
Publisher:
MIT Press
Publication Date:
2007
detail.hit.zdb_id:
1025692-1
detail.hit.zdb_id:
1498403-9
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