caa a22 4500 605478287 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10994-015-5497-9 doi (NATIONALLICENCE)springer-10.1007/s10994-015-5497-9 Generalized Twin Gaussian processes using Sharma-Mittal divergence [Elektronische Daten] [Mohamed Elhoseiny, Ahmed Elgammal] 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 Rényi, 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. The Author(s), 2015 Sharma-Mittal entropy nationallicence Structured regression nationallicence Twin Gaussian processes nationallicence Pose estimation nationallicence Image reconstruction nationallicence Elhoseiny Mohamed Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA aut Elgammal Ahmed Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA aut Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 399-424 0885-6125 100:2-3<399 2015 100 10994 https://doi.org/10.1007/s10994-015-5497-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5497-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Elhoseiny Mohamed Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA aut NATIONALLICENCE 700 1- Elgammal Ahmed Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 399-424 0885-6125 100:2-3<399 2015 100 10994