Matrix exponential based semi-supervised discriminant embedding for image classification
作者：
Highlights：
• An exponential semi-supervised discriminant embedding is proposed.
• It solves the small-sample-size problem.
• It enhances the discrimination between different classes.
• Classification performance after embedding is assessed on seven public image datasets.
• Performance is studied using several types of image descriptors.
摘要
Highlights•An exponential semi-supervised discriminant embedding is proposed.•It solves the small-sample-size problem.•It enhances the discrimination between different classes.•Classification performance after embedding is assessed on seven public image datasets.•Performance is studied using several types of image descriptors.
论文关键词：Graph-based semi-supervised learning,Small-sample-size (SSS) problem,Matrix exponential,Semi-supervised discriminant embedding (SDE),Distance diffusion mapping,Feature extraction,Image classification
论文评审过程：Received 15 April 2016, Revised 15 July 2016, Accepted 18 July 2016, Available online 20 July 2016, Version of Record 3 August 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.07.029
