On Clustering Histograms with k-Means by Using Mixed α-Divergences
AbstractClustering sets of histograms has become popular thanks to the success of the generic method of bag-of-X used in text categorization and in visual categorization applications. In this paper, we investigate the use of a parametric family of distortion measures, called the α-divergences, for clustering histograms. Since it usually makes sense to deal with symmetric divergences in information retrieval systems, we symmetrize the α -divergences using the concept of mixed divergences. First, we present a novel extension of k-means clustering to mixed divergences. Second, we extend the k-means++ seeding to mixed α-divergences and report a guaranteed probabilistic bound. Finally, we describe a soft clustering technique for mixed α-divergences. View Full-Text
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Nielsen, F.; Nock, R.; Amari, S.-I. On Clustering Histograms with k-Means by Using Mixed α-Divergences. Entropy 2014, 16, 3273-3301.
Nielsen F, Nock R, Amari S-I. On Clustering Histograms with k-Means by Using Mixed α-Divergences. Entropy. 2014; 16(6):3273-3301.Chicago/Turabian Style
Nielsen, Frank; Nock, Richard; Amari, Shun-ichi. 2014. "On Clustering Histograms with k-Means by Using Mixed α-Divergences." Entropy 16, no. 6: 3273-3301.