Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image

04/22/2020
by   Yaoming Cai, et al.
0

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace clustering has been proven to be powerful for exploiting the intrinsic relationship between data points. Despite the impressive performance in the HSI clustering, traditional subspace clustering methods often ignore the inherent structural information among data. In this paper, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering. Specifically, the framework recasts the self-expressiveness property of the data into the non-Euclidean domain, which results in a more robust graph embedding dictionary. We show that traditional subspace clustering models are the special forms of our framework with the Euclidean data. Basing on the framework, we further propose two novel subspace clustering models by using the Frobenius norm, namely Efficient GCSC (EGCSC) and Efficient Kernel GCSC (EKGCSC). Both models have a globally optimal closed-form solution, which makes them easier to implement, train, and apply in practice. Extensive experiments on three popular HSI datasets demonstrate that EGCSC and EKGCSC can achieve state-of-the-art clustering performance and dramatically outperforms many existing methods with significant margins.

READ FULL TEXT

page 1

page 8

page 9

research
05/15/2017

Kernel Truncated Regression Representation for Robust Subspace Clustering

Subspace clustering aims to group data points into multiple clusters of ...
research
09/05/2012

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

Under the framework of graph-based learning, the key to robust subspace ...
research
11/03/2020

Kernel Two-Dimensional Ridge Regression for Subspace Clustering

Subspace clustering methods have been widely studied recently. When the ...
research
01/18/2015

Correntropy Induced L2 Graph for Robust Subspace Clustering

In this paper, we study the robust subspace clustering problem, which ai...
research
11/15/2021

Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering

This paper presents FLGC, a simple yet effective fully linear graph conv...
research
05/05/2023

Adaptive Graph Convolutional Subspace Clustering

Spectral-type subspace clustering algorithms have shown excellent perfor...
research
10/28/2015

Fast Landmark Subspace Clustering

Kernel methods obtain superb performance in terms of accuracy for variou...

Please sign up or login with your details

Forgot password? Click here to reset