Scalable Optimal Margin Distribution Machine

Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the novel margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts. Nonetheless, it suffers from the ubiquitous scalability problem regarding both computation time and memory as other kernel methods. This paper proposes a scalable ODM, which can achieve nearly ten times speedup compared to the original ODM training method. For nonlinear kernels, we propose a novel distribution-aware partition method to make the local ODM trained on each partition be close and converge faster to the global one. When linear kernel is applied, we extend a communication efficient SVRG method to accelerate the training further. Extensive empirical studies validate that our proposed method is highly computational efficient and almost never worsen the generalization.

Authors

• Yilin Wang Adobe
• Nan Cao
• Teng Zhang Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device
• Xuanhua Shi Huazhong University of Science and Technology
• Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab, School of Computer Science and Technology, HUST

Keywords

• Data Mining: DM: Big data and scalability
• Machine Learning: ML: Classification
• Machine Learning: ML: Kernel methods