Generalized Twin Gaussian processes using Sharma-Mittal divergence
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| 003 | CHVBK | ||
| 005 | 20210128100405.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5497-9 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5497-9 | ||
| 245 | 0 | 0 | |a Generalized Twin Gaussian processes using Sharma-Mittal divergence |h [Elektronische Daten] |c [Mohamed Elhoseiny, Ahmed Elgammal] |
| 520 | 3 | |a 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. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Sharma-Mittal entropy |2 nationallicence | |
| 690 | 7 | |a Structured regression |2 nationallicence | |
| 690 | 7 | |a Twin Gaussian processes |2 nationallicence | |
| 690 | 7 | |a Pose estimation |2 nationallicence | |
| 690 | 7 | |a Image reconstruction |2 nationallicence | |
| 700 | 1 | |a Elhoseiny |D Mohamed |u Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA |4 aut | |
| 700 | 1 | |a Elgammal |D Ahmed |u Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 399-424 |x 0885-6125 |q 100:2-3<399 |1 2015 |2 100 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5497-9 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-015-5497-9 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Elhoseiny |D Mohamed |u Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Elgammal |D Ahmed |u Computer Science Department, Rutgers University, 110 Frelinghuysen Road, 08854-8019, Piscataway, NJ, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 399-424 |x 0885-6125 |q 100:2-3<399 |1 2015 |2 100 |o 10994 | ||