Support vector analysis of large-scale data based on kernels with iteratively increasing order

详细信息    查看全文

• 作者：Bo-Wei Chen ; Xinyu He ; Wen Ji ; Seungmin Rho ; Sun-Yuan Kung
• 关键词：Support vector analysis ; Big data analysis ; Kernel ridge regression (KRR) ; Ridge support vector machine (Ridge SVM)
• 刊名：The Journal of Supercomputing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：72
• 期：9
• 页码：3297-3311
• 全文大小：1,426 KB
• 刊物类别：Computer Science
• 刊物主题：Programming Languages, Compilers and Interpreters
  Processor Architectures
  Computer Science, general
  
• 出版者：Springer Netherlands
• ISSN：1573-0484
• 卷排序：72

文摘

This study presents an efficient approach for large-scale data training. To deal with the rapid growth of training complexity for big data analysis, a novel mechanism, which utilizes fast kernel ridge regression (Fast KRR) and ridge support vector machines (Ridge SVMs), is proposed in this study. Firstly, Fast KRR based on low-order intrinsic-space computation is developed. Preliminary support vectors are located by using Fast KRR. Subsequently, the system iteratively removes indiscriminant data until a Ridge SVM with a high-order kernel can accommodate the data size and generate a hyperplane. To speed up the removal of indiscriminant data, quick intrinsic-matrix rebuilding is devised in the iteration. Experiments on three databases were carried out for evaluating the proposed method. Moreover, different percentages of data removal were examined in the test. The results show that the performance is enhanced by as high as 78–152 folds. Besides, the mechanisms still maintain the accuracy. These findings thereby demonstrate the effectiveness of the proposed idea.