Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images

Abstract

In this paper, we present a graph representation that is based on the assumption that data live on a union of manifolds. Such a representation is based on sample proximities in reproducing kernel Hilbert spaces and is thus linear in the feature space and nonlinear in the original space. Moreover, it also expresses sample relationships under sparse and low… (More)
DOI: 10.1109/TGRS.2016.2517242

Topics

12 Figures and Tables

Statistics

010020020162017
Citations per Year

Citation Velocity: 55

Averaging 55 citations per year over the last 2 years.

Learn more about how we calculate this metric in our FAQ.

Cite this paper

@article{Morsier2016KernelLA, title={Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images}, author={Frank de Morsier and Maurice Borgeaud and Volker Gass and Jean-Philippe Thiran and Devis Tuia}, journal={IEEE Transactions on Geoscience and Remote Sensing}, year={2016}, volume={54}, pages={3410-3420} }