Multiclass learning with simplex coding
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Poggio_Multiclass.pdf
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Author(s)
Mroueh, Youssef
Poggio, Tomaso A.
Rosasco, Lorenzo Andrea
Slotine, Jean-Jacques E.
Date Issued
September 2012
Journal
Advances in Neural Information Processing Systems (NIPS)
Publisher
Neural Information Processing Systems Foundation
Citation
Mroueh, Youssef, Tomaso Poggio, Lorenzo Rosasco, and Jean-Jacques E. Slotine. "Multiclass learning with simplex coding." Advances in Neural Information Processing Systems 25 (2012).
Version
Author's final manuscript
Abstract
In this paper we discuss a novel framework for multiclass learning, defined by a suitable coding/decoding strategy, namely the simplex coding, that allows us to generalize to multiple classes a relaxation approach commonly used in binary classification. In this framework, we develop a relaxation error analysis that avoids constraints on the considered hypotheses class. Moreover, using this setting we derive the first provably consistent regularized method with training/tuning complexity that is independent to the number of classes. We introduce tools from convex analysis that can be used beyond the scope of this paper.
MIT Department
Massachusetts Institute of Technology. Center for Biological & Computational Learning
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
McGovern Institute for Brain Research at MIT
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