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Combination of Classification and Clustering Results with Label Propagation


Abstract:

This letter considers the combination of multiple classification and clustering results to improve the prediction accuracy. First, an object-similarity graph is construct...Show More

Abstract:

This letter considers the combination of multiple classification and clustering results to improve the prediction accuracy. First, an object-similarity graph is constructed from multiple clustering results. The labels predicted by the classification models are then propagated on this graph to adaptively satisfy the smoothness of the prediction over the graph. The convex learning problem is efficiently solved by the label propagation algorithm. A semi-supervised extension is also provided to further improve the performance. Experiments on 11 tasks identify the validity of the proposed models.
Published in: IEEE Signal Processing Letters ( Volume: 21, Issue: 5, May 2014)
Page(s): 610 - 614
Date of Publication: 14 March 2014

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