Semi-supervised linear discriminant analysis for dimension reduction and classification

作者：

Highlights：

• A semi-supervised variant of LDA named semi-supervised LDA is proposed.

• Our method can use limited number of labeled data and a quantity of the unlabeled ones for training.

• We develop an iterative algorithm which calculates the class indicator matrix and the projection alternatively.

摘要

Highlights•A semi-supervised variant of LDA named semi-supervised LDA is proposed.•Our method can use limited number of labeled data and a quantity of the unlabeled ones for training.•We develop an iterative algorithm which calculates the class indicator matrix and the projection alternatively.

论文关键词：Dimension reduction,Semi-supervised learning,Linear discriminant analysis,Data classification

论文评审过程：Received 30 July 2015, Revised 12 November 2015, Accepted 24 February 2016, Available online 8 March 2016, Version of Record 6 May 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.02.019