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

Bo Wei Chen, Xinyu He, Wen Ji, Seungmin Rho, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3297-3311
Number of pages15
JournalJournal of Supercomputing
Issue number9
StatePublished - Sep 1 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture


  • Big data analysis
  • Kernel ridge regression (KRR)
  • Ridge support vector machine (Ridge SVM)
  • Support vector analysis


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