Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Recognition invariance obtained by extended and invariant features
Neural Networks - 2004 Special issue Vision and brain
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bilinear Sparse Coding for Invariant Vision
Neural Computation
Supervised self-taught learning: actively transferring knowledge from unlabeled data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Image feature extraction using the fusion features of BEMD and WCB-NNSC
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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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.