Least Squares Twin Support Vector Machines Based on Sample Reduction for Hyperspectral Image Classification
Liguo Wang, Ting-ting Lu, Yueshuang Yang · 2015
To overcome the low efficiency of Least Squares Twin Support Vector Machine (LSTSVM) in classifying, a new method called Sample Reduction LSTSVM (SR-LSTSVM) is proposed.The method greatly reduces the training samples and so improves the speed of LSTSVM, while the ability of LSTSVM to classify is unaffected.Our experiment results show remarkable improvement of the speed of LSTSVM on hyperspectral image, supporting our idea.