A kernel path algorithm for general parametric quadratic programming problem
作者：
Highlights：
• We study a general PQP problem that can be instantiated into many learning problems.
• Based on the general PQP problem, we provide a unified and robust kernel path implementation (i.e. GKP) for an extensive number of PQP problems, many of which still do not have kernel path algorithms.
• We analyze the iterative complexity and computational complexity of GKP.
• We conduct experiments on various datasets, these results not only confirm the identity between GKP and several exiting specific kernel path algorithms (SKP), but also show that our GKP is superior to SKP in terms of generality and robustness.
摘要
•We study a general PQP problem that can be instantiated into many learning problems.•Based on the general PQP problem, we provide a unified and robust kernel path implementation (i.e. GKP) for an extensive number of PQP problems, many of which still do not have kernel path algorithms.•We analyze the iterative complexity and computational complexity of GKP.•We conduct experiments on various datasets, these results not only confirm the identity between GKP and several exiting specific kernel path algorithms (SKP), but also show that our GKP is superior to SKP in terms of generality and robustness.
论文关键词：Kernel path,QR decomposition,Parametric quadratic programming,Cross validation
论文评审过程：Received 30 April 2020, Revised 1 October 2020, Accepted 7 March 2021, Available online 16 March 2021, Version of Record 23 March 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107941
