Abstract
Although the basic sparse coding model has been quite successful at explaining the receptive fields of simple cells in V1, it ignores an important constrain: perception task. We put forward a novel sparse coding model, called task-oriented sparse coding (TOSC) model, combining the discriminability constrain supervised by classification task, besides the sparseness criteria. Simulation experiments are performed using real images including class of scene and class of building. The results show that TOSC can organize some significant receptive fields with distinct topological structure which will favor the classification task. Moreover, the coefficients of TOSC notablely improve the classification accuracy, from the 53.5% of pixel-based model to 86.7%, in the case of none distinct damage on the performance of reconstruction error and sparseness. TOSC model, complementing the feedback sparse coding model, is more consistent with biological mechanism, and shows good potential in the feature extraction for pattern classification.
This paper is supported by National Natural Science Foundation of China No. 60435010 and National Basic Research Priorities Programme No. 2003CB317004.
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Li, Q., Lin, D., Shi, Z. (2005). Task-Oriented Sparse Coding Model for Pattern Classification. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_121
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DOI: https://doi.org/10.1007/11539087_121
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