TY - GEN
T1 - Alternating direction method of multipliers for regularized multiclass support vector machines
AU - Xu, Yangyang
AU - Akrotirianakis, Ioannis
AU - Chakraborty, Amit
PY - 2015/1/1
Y1 - 2015/1/1
N2 - The support vector machine (SVM) was originally designed for binary classifications. A lot of effort has been put to generalize the binary SVM to multiclass SVM (MSVM) which are more complex problems. Initially, MSVMs were solved by considering their dual formulations which are quadratic programs and can be solved by standard second-order methods. However, the duals of MSVMs with regularizers are usually more difficult to formulate and computationally very expensive to solve. This paper focuses on several regularized MSVMs and extends the alternating direction method of multiplier (ADMM) to these MSVMs. Using a splitting technique, all considered MSVMs are written as two-block convex programs, for which the ADMM has global convergence guarantees. Numerical experiments on synthetic and real data demonstrate the high efficiency and accuracy of our algorithms.
AB - The support vector machine (SVM) was originally designed for binary classifications. A lot of effort has been put to generalize the binary SVM to multiclass SVM (MSVM) which are more complex problems. Initially, MSVMs were solved by considering their dual formulations which are quadratic programs and can be solved by standard second-order methods. However, the duals of MSVMs with regularizers are usually more difficult to formulate and computationally very expensive to solve. This paper focuses on several regularized MSVMs and extends the alternating direction method of multiplier (ADMM) to these MSVMs. Using a splitting technique, all considered MSVMs are written as two-block convex programs, for which the ADMM has global convergence guarantees. Numerical experiments on synthetic and real data demonstrate the high efficiency and accuracy of our algorithms.
KW - Alternating direction method of multipliers
KW - Elastic net
KW - Group lasso
KW - Multiclass classification
KW - Supnorm
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84955317353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955317353&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27926-8_10
DO - 10.1007/978-3-319-27926-8_10
M3 - Conference contribution
AN - SCOPUS:84955317353
SN - 9783319279251
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 105
EP - 117
BT - Machine Learning, Optimization, and Big Data - 1st International Workshop, MOD 2015 Taormina, Revised Selected Papers
A2 - Pavone, Mario
A2 - Farinella, Giovanni Maria
A2 - Cutello, Vincenzo
A2 - Pardalos, Panos
PB - Springer- Verlag
T2 - 1st International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015
Y2 - 21 July 2015 through 23 July 2015
ER -