Density-Dependent Quantized Least Squares Support Vector Machine for Large Data Sets

作者：Nan, Shengyu; Sun, Lei; Chen, Badong^*; Lin, Zhiping; Toh, Kar-Ann

来源：IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(1): 94-106.

DOI：10.1109/TNNLS.2015.2504382

• Data density dependence
• large data
• sparse representation
• support vector machine (SVM)
• vector quantization

Based on the knowledge that input data distribution is important for learning, a data density-dependent quantization scheme (DQS) is proposed for sparse input data representation. The usefulness of the representation scheme is demonstrated by using it as a data preprocessing unit attached to the well-known least squares support vector machine (LS-SVM) for application on big data sets. Essentially, the proposed DQS adopts a single shrinkage threshold to obtain a simple quantization scheme, which adapts its outputs to input data density. With this quantization scheme, a large data set is quantized to a small subset where considerable sample size reduction is generally obtained. In particular, the sample size reduction can save significant computational cost when using the quantized subset for feature approximation via the Nystrom method. Based on the quantized subset, the approximated features are incorporated into LS-SVM to develop a data density-dependent quantized LS-SVM (DQLS-SVM), where an analytic solution is obtained in the primal solution space. The developed DQLS-SVM is evaluated on synthetic and benchmark data with particular emphasis on large data sets. Extensive experimental results show that the learning machine incorporating DQS attains not only high computational efficiency but also good generalization performance.

• 出版日期2017-1
• 单位北京理工大学; 西安交通大学