On incremental semi-supervised discriminant analysis

作者：

Highlights：

• Incremental semi-supervised discriminant analysis algorithm is proposed.

• Large unlabeled data is utilized to estimate total scatter in discriminant analysis.

• It does not require to incrementally update the total scatter eigenmodel.

• A face recognition case study is shown on CMU-PIE, NIR-VIS-2.0, and MultiPIE databases.

• The proposed ISSDA requires significantly less computational time and maintains accuracy.

摘要

Highlights•Incremental semi-supervised discriminant analysis algorithm is proposed.•Large unlabeled data is utilized to estimate total scatter in discriminant analysis.•It does not require to incrementally update the total scatter eigenmodel.•A face recognition case study is shown on CMU-PIE, NIR-VIS-2.0, and MultiPIE databases.•The proposed ISSDA requires significantly less computational time and maintains accuracy.

论文关键词：Incremental learning,Semi-supervised learning,Discriminant analysis,Face recognition

论文评审过程：Received 27 May 2015, Revised 19 September 2015, Accepted 24 September 2015, Available online 9 October 2015, Version of Record 24 December 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.09.030