Title :
MKPM: A multiclass extension to the kernel projection machine
Author :
Takerkart, Sylvain ; Ralaivola, Liva
Author_Institution :
Inst. de Neurosciences Cognitives de la Mediterranee, Aix-Marseille Univ., Marseille, France
Abstract :
We introduce Multiclass Kernel Projection Machines (MKPM), a new formalism that extends the Kernel Projection Machine framework to the multiclass case. Our formulation is based on the use of output codes and it implements a co-regularization scheme by simultaneously constraining the projection dimensions associated with the individual predictors that constitute the global classifier. In order to solve the optimization problem posed by our formulation, we propose an efficient dynamic programming approach. Numerical simulations conducted on a few pattern recognition problems illustrate the soundness of our approach.
Keywords :
dynamic programming; pattern classification; MKPM; dynamic programming approach; multiclass extension; multiclass kernel projection machine; optimization problem; pattern recognition problem; Dynamic programming; Encoding; Kernel; Machine learning; Minimization; Numerical simulation; Training;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4577-0394-2
DOI :
10.1109/CVPR.2011.5995657