Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images
- 8 February 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 54 (6), 3410-3420
- https://doi.org/10.1109/tgrs.2016.2517242
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-rank constraints, meaning that the resulting graph will have limited connectivity (sparseness) and that samples belonging to the same group will be likely to be connected together and not with those from other groups (low rankness). We present this graph representation as a general representation that can be then applied to any graph-based method. In the experiments, we consider the clustering of hyperspectral images and semi-supervised classification (one class and multiclass).Keywords
Funding Information
- Swiss National Science Foundation (PP00P2_150593)
- RUAG Schweiz AG.
This publication has 44 references indexed in Scilit:
- A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale DataIEEE Transactions on Neural Networks and Learning Systems, 2015
- Non-linear low-rank and sparse representation for hyperspectral image analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- A simplified low rank and sparse graph for semi-supervised learningNeurocomputing, 2014
- Structured Priors for Sparse-Representation-Based Hyperspectral Image ClassificationIEEE Geoscience and Remote Sensing Letters, 2013
- Low rank subspace clustering (LRSC)Pattern Recognition Letters, 2013
- Sparse Subspace Clustering: Algorithm, Theory, and ApplicationsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- Hyperspectral Image Classification via Kernel Sparse RepresentationIEEE Transactions on Geoscience and Remote Sensing, 2012
- L1-graph semisupervised learning for hyperspectral image classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Hyperspectral Image Classification Using Dictionary-Based Sparse RepresentationIEEE Transactions on Geoscience and Remote Sensing, 2011
- Learning Sparse Codes for Hyperspectral ImageryIEEE Journal of Selected Topics in Signal Processing, 2011