Journal of Optimization Theory and Applications
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Regions and Patches for Object Class Localization
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Classification using discriminative restricted Boltzmann machines
Proceedings of the 25th international conference on Machine learning
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
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The k q-flats algorithm is a generalization of the popular k-means algorithm where q dimensional best fit affine sets replace centroids as the cluster prototypes. In this work, a modification of the k q-flats framework for pattern classification is introduced. The basic idea is to replace the original reconstruction only energy, which is optimized to obtain the k affine spaces, by a new energy that incorporates discriminative terms. This way, the actual classification task is introduced as part of the design and optimization. The presentation of the proposed framework is complemented with experimental results, showing that the method is computationally very efficient and gives excellent results on standard supervised learning benchmarks.