Training SVMs on a bound vectors set based on Fisher projection

作者：Yu, Xu^*; Yang, Jing; Xie, Zhiqiang

来源：Frontiers of Computer Science, 2014, 8(5): 793-806.

DOI：10.1007/s11704-014-3161-3

• support vector machines
• bound vectors set
• Fisher discriminant
• sequential minimal optimization

Standard support vector machines (SVMs) training algorithms have O(l(3)) computational and O(l(2)) space complexities, where l is the training set size. It is thus computationally infeasible on very large data sets. To alleviate the computational burden in SVM training, we propose an algorithm to train SVMs on a bound vectors set that is extracted based on Fisher projection. For linear separate problems, we use linear Fisher discriminant to compute the projection line, while for non-linear separate problems, we use kernel Fisher discriminant to compute the projection line. For each case, we select a certain ratio samples whose projections are adjacent to those of the other class as bound vectors. Theoretical analysis shows that the proposed algorithm is with low computational and space complexities. Extensive experiments on several classification benchmarks demonstrate the effectiveness of our approach.

• 出版日期2014-10
• 单位哈尔滨学院; 哈尔滨工程大学; 青岛科技大学