Generating the reduced set by systematic sampling

Chien Chung Chang*, Yuh-Jye Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The computational difficulties occurred when we use a conventional support vector machine with nonlinear kernels to deal with massive datasets. The reduced support vector machine (RSVM) replaces the fully dense square kernel matrix with a small rectangular, kernel matrix which is used in the nonlinear SVM formulation to avoid the computational difficulties. In this paper, we propose a new algorithm Systematic Sampling RSVM (SSRSVM) that selects the informative data points to form the reduced set while the RSVM used random selection scheme. This algorithm is inspired by the key idea of SVM, the SVM classifier can be represented by support vectors and the misclassified points are a part of support vectors. SSRSVM starts with an extremely small initial reduced set and adds a portion of misclassified points into the reduced set iteratively based on the current classifier until the validation- set correctness is large enough. In our experiments, we tested SSRSVM on six public available datasets. It turns out that SSRSVM might automatically generate a smaller size of reduced set than the one by random sampling. Moreover, SSRSVM is faster than RSVM and much fasteitahan conventional SVM under the same level of the test set correctness.


  • Kernel function
  • Reduced set
  • Reduced support vector machine
  • Support vector machine
  • Systematic sampling

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