Convex support and Relevance Vector Machines for selective multimodal pattern recognition
Seredin, O, Mottl, V, Tatarchuk, A, Razin, N and Windridge, D (2012) Convex support and Relevance Vector Machines for selective multimodal pattern recognition Pattern Recognition (ICPR), 2012 21st International Conference on. pp. 1647-1650.
Available under License : See the attached licence file.
We address the problem of featureless pattern recognition under the assumption that pair-wise comparison of objects is arbitrarily scored by real numbers. Such a linear embedding is much more general than the traditional kernel-based approach, which demands positive semi-definiteness of the matrix of object comparisons. This demand is frequently prohibitive and is further complicated if there exist a large number of comparison functions, i.e., multiple modalities of object representation. In these cases, the experimenter typically also has the problem of eliminating redundant modalities and objects. In the context of the general pair-wise comparison space this problem becomes mathematically analogous to that of wrapper-based feature selection. The resulting convex SVM-like training criterion is analogous to Tipping's Relevance Vector Machine, but essentially generalizes it via the presence of a structural parameter controlling the selectivity level. © 2012 ICPR Org Committee.
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Additional Information :||© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||04 Mar 2014 11:27|
|Last Modified :||09 Jun 2014 13:31|
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