Inductive and flexible feature extraction for semi-supervised pattern categorization
作者：
Highlights：
• A flexible graph-based semi-supervised embedding is proposed.
• A kernel version of the linear semi-supervised algorithm is also proposed.
• They simultaneously estimate a non-linear embedding and its out-of-sample extension.
• Classification performance after embedding is assessed on ten benchmark datasets.
• We use KNN, SVM, and two phase test sample sparse representation as classifiers.
摘要
Highlights•A flexible graph-based semi-supervised embedding is proposed.•A kernel version of the linear semi-supervised algorithm is also proposed.•They simultaneously estimate a non-linear embedding and its out-of-sample extension.•Classification performance after embedding is assessed on ten benchmark datasets.•We use KNN, SVM, and two phase test sample sparse representation as classifiers.
论文关键词：Feature extraction,Semi-supervised discriminant analysis,Graph-based embedding,Out-of-sample extension,Pattern categorization
论文评审过程：Received 17 June 2015, Revised 10 December 2015, Accepted 28 April 2016, Available online 22 May 2016, Version of Record 7 June 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.04.024
