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Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization

Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not necessarily be reconstructed well by standard clustering method...

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Detalles Bibliográficos
Autor principal: Fernsel, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540947/
https://www.ncbi.nlm.nih.gov/pubmed/34677280
http://dx.doi.org/10.3390/jimaging7100194
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author Fernsel, Pascal
author_facet Fernsel, Pascal
author_sort Fernsel, Pascal
collection PubMed
description Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not necessarily be reconstructed well by standard clustering methods. Motivated by applications in hyperspectral imaging, we introduce in this work clustering models based on Orthogonal Nonnegative Matrix Factorization (ONMF), which include an additional Total Variation (TV) regularization procedure on the cluster membership matrix to enforce the needed spatial coherence in the clusters. We propose several approaches with different optimization techniques, where the TV regularization is either performed as a subsequent post-processing step or included into the clustering algorithm. Finally, we provide a numerical evaluation of 12 different TV regularized ONMF methods on a hyperspectral dataset obtained from a matrix-assisted laser desorption/ionization imaging measurement, which leads to significantly better clustering results compared to classical clustering models.
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spelling pubmed-85409472021-10-28 Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization Fernsel, Pascal J Imaging Article Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not necessarily be reconstructed well by standard clustering methods. Motivated by applications in hyperspectral imaging, we introduce in this work clustering models based on Orthogonal Nonnegative Matrix Factorization (ONMF), which include an additional Total Variation (TV) regularization procedure on the cluster membership matrix to enforce the needed spatial coherence in the clusters. We propose several approaches with different optimization techniques, where the TV regularization is either performed as a subsequent post-processing step or included into the clustering algorithm. Finally, we provide a numerical evaluation of 12 different TV regularized ONMF methods on a hyperspectral dataset obtained from a matrix-assisted laser desorption/ionization imaging measurement, which leads to significantly better clustering results compared to classical clustering models. MDPI 2021-09-28 /pmc/articles/PMC8540947/ /pubmed/34677280 http://dx.doi.org/10.3390/jimaging7100194 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fernsel, Pascal
Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
title Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
title_full Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
title_fullStr Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
title_full_unstemmed Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
title_short Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization
title_sort spatially coherent clustering based on orthogonal nonnegative matrix factorization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540947/
https://www.ncbi.nlm.nih.gov/pubmed/34677280
http://dx.doi.org/10.3390/jimaging7100194
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