Time series cluster kernels to exploit informative missingness and incomplete label information
作者：
Highlights：
• Two novel kernels for multivariate time series with missing data are proposed.
• Informative missingness is exploited using mixed mode Bayesian mixture models.
• We exploit incomplete label information using ideas from information theory.
• A case study of patients suffering from infectious postoperative complications.
摘要
•Two novel kernels for multivariate time series with missing data are proposed.•Informative missingness is exploited using mixed mode Bayesian mixture models.•We exploit incomplete label information using ideas from information theory.•A case study of patients suffering from infectious postoperative complications.
论文关键词：Multivariate time series,Kernel methods,Missing data,Informative missingness,Semi-supervised learning
论文评审过程：Received 27 November 2018, Revised 11 November 2020, Accepted 8 February 2021, Available online 20 February 2021, Version of Record 2 March 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107896
