Low-Density Cut Based Tree Decomposition for Large-Scale SVM Problems

Lifang He, Hong-Han Shuai, Xiangnan Kong, Zhifeng Hao, Xiaowei Yang, Philip S. Yu

Abstract: The current trend of growth of information reveals that it is inevitable that large-scale learning problems become the norm. In this paper, we propose and analyze a novel Low-density Cut based tree Decomposition method for large-scale SVM problems, called LCD-SVM. The basic idea here is divide and conquer: use a decision tree to decompose the data space and train SVMs on the decomposed regions. Specifically, we demonstrate the application of low density separation principle to devise a splitting criterion for rapidly generating a high-quality tree, thus maximizing the benefits of SVMs training. Extensive experiments on 14 real-world datasets show that our approach can provide a significant improvement in training time over state-of-the-art methods while keeps comparable test accuracy with other methods, especially for very large-scale datasets.