Parallelizing Support Vector Machines on Distributed Computers

Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let n denote the number of training instances, p the reduced matrix dimension after factorization (p is significantly smaller than n), and m the number of machines. PSVM reduces the memory requirement from O(n2 ) to O(np/m), and improves computation time to O(np2 /m). Empirical study shows PSVM to be effective. PSVM Open Source is available for download at http://code.google.com/p/psvm/.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui
• Keywords: Approximate Matrix Factorization, Information Technology, NIPS 2007, Parallel Svm Algorithm, Support Vector Machines