FRSVC: Towards making support vector clustering consume less
作者：
Highlights：
• Pricey storage and computation consumptions frustrate SVC’s application on limited platforms.
• We propose FRSVC: towards making SVC consume less and affordable for any platform.
• A reformative solver for dual problem is proposed based on dual coordinate descent method.
• Sample once with connection checking first strategy is designed for a faster labeling phase.
• Results confirm the superiority of FRSVC under limited-memory constraints.
摘要
•Pricey storage and computation consumptions frustrate SVC’s application on limited platforms.•We propose FRSVC: towards making SVC consume less and affordable for any platform.•A reformative solver for dual problem is proposed based on dual coordinate descent method.•Sample once with connection checking first strategy is designed for a faster labeling phase.•Results confirm the superiority of FRSVC under limited-memory constraints.
论文关键词：Large-scale data,Support vector clustering,Dual coordinate descent,Sampling strategy
论文评审过程：Received 31 July 2016, Revised 12 February 2017, Accepted 26 April 2017, Available online 27 April 2017, Version of Record 4 May 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.04.025
