Fuzzy semi-supervised weighted linear loss twin support vector clustering
作者：
Highlights：
• Introduced continuous Loss function to build modified WLL-TWSVC algorithm.
• Proposed Semi-Supervised WLL-TWSVC method for semi supervised multi-category clustering problems.
• Introduced Fuzzy Laplacian matrix to build F-Lap-WLL-TWSVC algorithm for semi-supervised multi-category clustering problems.
• Presented application of proposed algorithm in image segmentation.
• Solution is obtained via solving a series of system of linear equations only.
摘要
•Introduced continuous Loss function to build modified WLL-TWSVC algorithm.•Proposed Semi-Supervised WLL-TWSVC method for semi supervised multi-category clustering problems.•Introduced Fuzzy Laplacian matrix to build F-Lap-WLL-TWSVC algorithm for semi-supervised multi-category clustering problems.•Presented application of proposed algorithm in image segmentation.•Solution is obtained via solving a series of system of linear equations only.
论文关键词：Twin Support Vector Clustering,Weighted linear loss function,Semi-supervised clustering,Plane based clustering,Fuzzy membership matrix
论文评审过程：Received 27 July 2018, Revised 16 November 2018, Accepted 19 November 2018, Available online 30 November 2018, Version of Record 7 January 2019.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.11.027
