Multiclass learning with simplex coding
Author(s)
Mroueh, Youssef; Poggio, Tomaso A.; Rosasco, Lorenzo Andrea; Slotine, Jean-Jacques E.
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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.
Date issued
2012-09Department
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 MITJournal
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
ISSN
1049-5258