Fast semi-supervised discriminant analysis for binary classification of large data sets

作者：

Highlights：

• Computationally more efficient algorithm for semi-supervised discriminant analysis.

• Efficient handling of regularization parameter.

• Industry-scale data sets from chemogenomics analysed in a few seconds.

摘要

•Computationally more efficient algorithm for semi-supervised discriminant analysis.•Efficient handling of regularization parameter.•Industry-scale data sets from chemogenomics analysed in a few seconds.

论文关键词：Semi-supervised learning,Semi-supervised discriminant analysis,Large-scale

论文评审过程：Received 10 July 2017, Revised 16 January 2019, Accepted 19 February 2019, Available online 19 February 2019, Version of Record 22 February 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.02.015