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Classification and Clustering via Dictionary Learning with Structured Incoherence

10 years 3 months ago
Classification and Clustering via Dictionary Learning with Structured Incoherence
A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are best reconstructed in a sparse coding manner. Thereby, we are modeling the data as a union of learned low dimensional subspaces, and data points associated to subspaces spanned by just a few atoms of the same learned dictionary are clustered together. An incoherence promoting term encourages dictionaries associated to different classes to be as independent as possible, while still allowing for different classes to share features. This term directly acts on the dictionaries, thereby being applicable both in the supervised and unsupervised settings. Using learned dictionaries for classification and clustering makes this method robust...
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapir
Added 03 Sep 2010
Updated 03 Sep 2010
Type Conference
Year 2010
Where CVPR
Authors Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapiro
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