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• W1576564833 abstract "There has been a growing interest in mutual information measures due to their wide range of applications in machine learning and computer vision. In this paper, we present a generalized structured regression framework based on Sharma---Mittal (SM) divergence, a relative entropy measure, which is introduced to in the machine learning community in this work. SM divergence is a generalized mutual information measure for the widely used Renyi, Tsallis, Bhattacharyya, and Kullback---Leibler (KL) relative entropies. Specifically, we study SM divergence as a cost function in the context of the Twin Gaussian processes (TGP) (Bo and Sminchisescu 2010), which generalizes over the KL-divergence without computational penalty. We show interesting properties of Sharma---Mittal TGP (SMTGP) through a theoretical analysis, which covers missing insights in the traditional TGP formulation. However, we generalize this theory based on SM-divergence instead of KL-divergence which is a special case. Experimentally, we evaluated the proposed SMTGP framework on several datasets. The results show that SMTGP reaches better predictions than KL-based TGP, since it offers a bigger class of models through its parameters that we learn from the data." @default.
• W1576564833 created "2016-06-24" @default.
• W1576564833 creator A5039640321 @default.
• W1576564833 creator A5085089542 @default.
• W1576564833 date "2015-06-23" @default.
• W1576564833 modified "2023-09-26" @default.
• W1576564833 title "Generalized Twin Gaussian processes using Sharma–Mittal divergence" @default.
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• W1576564833 doi "https://doi.org/10.1007/s10994-015-5497-9" @default.
• W1576564833 hasPublicationYear "2015" @default.
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