Efficient Data Point Pruning for One-Class SVM

  • Yasuhiro Fujiwara NTT Software Innovation Center
  • Sekitoshi Kanai NTT Software Innovation Center
  • Junya Arai Nippon Telegraph and Telephone
  • Yasutoshi Ida Nippon Telegraph and Telephone
  • Naonori Ueda Nippon Telegraph and Telephone


One-class SVM is a popular method for one-class classification but it needs high computation cost. This paper proposes Quix as an efficient training algorithm for one-class SVM. It prunes unnecessary data points before applying the SVM solver by computing upper and lower bounds of a parameter that determines the hyper-plane. Since we can efficiently check optimality of the hyper-plane by using the bounds, it guarantees the identical classification results to the original approach. Experiments show that it is up to 6800 times faster than existing approaches without degrading optimality.

AAAI Technical Track: Machine Learning