Fast algorithms for incremental and decremental semi-supervised discriminant analysis
作者：
Highlights：
• First, a new incremental semi-supervised discriminant analysis method is proposed, in which we consider updating the total scatter matrix and the between-class scatter matrix simultaneous when new samples are added.
• Second, we show how to solve the eigenproblem of the updated total scatter matrix efficiently, by using the economic QR decomposition.
• Third, we propose two decremental algorithms for semi-supervised discriminant analysis, which can eliminate the negative effects from erroneous or distorted tags with little computational cost.
摘要
•First, a new incremental semi-supervised discriminant analysis method is proposed, in which we consider updating the total scatter matrix and the between-class scatter matrix simultaneous when new samples are added.•Second, we show how to solve the eigenproblem of the updated total scatter matrix efficiently, by using the economic QR decomposition.•Third, we propose two decremental algorithms for semi-supervised discriminant analysis, which can eliminate the negative effects from erroneous or distorted tags with little computational cost.
论文关键词：Dimensionality reduction,Semi-supervised discriminant analysis,Incremental learning,Decremental learning,Modified total scatter matrix
论文评审过程：Received 13 October 2021, Revised 13 June 2022, Accepted 3 July 2022, Available online 5 July 2022, Version of Record 9 July 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108888
