Indefinite Core Vector Machine
作者：
Highlights：
• Indefinite Core Vector Machine (iCVM) is proposed.
• Approximation concepts are provided leading to linear runtime complexity under moderate assumptions.
• Sparsification of iCVM is proposed showing that in many cases also a low memory complexity can be obtained with an acceptable loss in accuracy.
• The algorithm is compared to a number of related methods and multiple datasets showing competitive performance but with much lower computational and memory complexity.
摘要
•Indefinite Core Vector Machine (iCVM) is proposed.•Approximation concepts are provided leading to linear runtime complexity under moderate assumptions.•Sparsification of iCVM is proposed showing that in many cases also a low memory complexity can be obtained with an acceptable loss in accuracy.•The algorithm is compared to a number of related methods and multiple datasets showing competitive performance but with much lower computational and memory complexity.
论文关键词：Indefinite learning,Krĕin space,Classification,Core Vector Machine,Nyström,Sparse,Linear complexity
论文评审过程：Received 27 December 2016, Revised 6 April 2017, Accepted 1 June 2017, Available online 3 June 2017, Version of Record 13 June 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.06.003
