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Kernel low-rank and sparse graph for unsupervised and semi-supervised classification of hyperspectral images


de Morsier, Frank; Borgeaud, Maurice; Gass, Volker; Thiran, Jean-Philippe; Tuia, Devis (2016). Kernel low-rank and sparse graph for unsupervised and semi-supervised classification of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 54(6):3410-3420.

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).

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).

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1 citation in Web of Science®
3 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2016
Deposited On:10 Oct 2016 12:51
Last Modified:10 Oct 2016 12:51
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Publisher DOI:https://doi.org/10.1109/TGRS.2016.2517242

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