Multiview Locally Linear Embedding for Spectral-Spatial Dimensionality Reduction of Hyperspectral Imagery  

作　　者：Haochen Ji Zongyu Zuo 

机构地区：[1]Seventh Research Division,Beihang University(BUAA),Beijing 100191,China [2]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第6期1091-1094,共4页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62073019)。

摘　　要：Dear Editor,Dimensionality reduction(DR)plays a prominent role in the processing of hyperspectral imagery.Considering the high dimensionality of multiple features,this letter presents a new unsupervised DR method named multiview locally linear embedding(MLLE),which captures the local linearity and global nonlinearity of the data sufficiently.We formulate MLLE as an optimization problem,where the diversity and complementarity of multiple features is fully exploited.An effective alternating optimization scheme is derived,and a linear model based on ridge regression is extended to alleviate the high correlation among single-view features.Experimental results on the Indian Pines and Pavia University datasets demonstrate the superiority of our proposed MLLE.

关 键 词：LOCALLY NONLINEARITY LETTER 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]