Laplacian Regularized Spatial-Aware Collaborative Competitive Representation for Hyperspectral Dimensionality Reduction

Recently, graph-based methods have drawn increased attention for representing a high-dimensional features into a low- dimensional data. To obtain an optimal transform for the purpose of classification, different collaborative representation-based methods are for dimensionality reduction (DR). In previous work, a spatial-aware collaborative competitive representation (SaCCPGT) based unsupervised method was investigated for DR of hyperspectral imagery (HSI). It incorporates spatial information into the representation framework. However, it can be further enhanced by considering the data manifold structure. In this paper, Laplacian regularized SaCCPGT (LapSaCCPGT) is presented for DR of HSI to better utilize data structure information into the representation framework. The experimental results observed on different hyperspectral datasets demonstrate the superiority of the proposed LapSaCCPGT than the state-of-the-art DR methods.