Multi-view K-Means Clustering with Bregman Divergences

2018 ◽  
pp. 26-38
Author(s):  
Yan Wu ◽  
Liang Du ◽  
Honghong Cheng
Keyword(s):  
Bregman Divergences

On the uniform concentration bounds and large sample properties of clustering with Bregman divergences

Stat ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Debolina Paul ◽  
Saptarshi Chakraborty ◽  
Swagatam Das
Keyword(s):  
Large Sample ◽  
Bregman Divergences ◽  
Concentration Bounds

scholarly journals On a Generalization of the Jensen–Shannon Divergence and the Jensen–Shannon Centroid

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 221 ◽  
Author(s):  
Frank Nielsen
Keyword(s):  
Iterative Algorithm ◽  
Bregman Divergences ◽  
Leibler Divergence ◽  
Probability Densities ◽  
Jensen Shannon Divergence

The Jensen–Shannon divergence is a renown bounded symmetrization of the Kullback–Leibler divergence which does not require probability densities to have matching supports. In this paper, we introduce a vector-skew generalization of the scalar α -Jensen–Bregman divergences and derive thereof the vector-skew α -Jensen–Shannon divergences. We prove that the vector-skew α -Jensen–Shannon divergences are f-divergences and study the properties of these novel divergences. Finally, we report an iterative algorithm to numerically compute the Jensen–Shannon-type centroids for a set of probability densities belonging to a mixture family: This includes the case of the Jensen–Shannon centroid of a set of categorical distributions or normalized histograms.

scholarly journals Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

2016 ◽  
Vol 27 (6) ◽  
pp. 1294-1306 ◽  
Author(s):  
Mehrtash T. Harandi ◽  
Richard Hartley ◽  
Brian Lovell ◽  
Conrad Sanderson
Keyword(s):  
Sparse Coding ◽  
Positive Definite ◽  
Symmetric Positive Definite ◽  
Bregman Divergences
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