Collaborative and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery

Sparse graph-based discriminant analysis has drawn much attention to represent high dimensional data into low-dimensional subspace by using
$l_{1}$
-norm optimization. There exists sparse and low-rank graph-based discriminant analysis (SLGDA) for incorporating local and global data structure together by combining both sparsity and low-rankness. Deviating from the concept of sparse representation, collaborative and low-rank representation-based discriminant analysis is proposed (CLGDA) in this paper with the assumption that collaboration among atoms is more important to estimate appropriate representation coefficients by accommodating within-class variation. The experimental results obtained on several hyperspectral datasets illustrate the superior classification performance of the proposed CLGDA in comparison to SLGDA and state-of-the-art approaches.