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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8540947 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fernselpascal spatiallycoherentclusteringbasedonorthogonalnonnegativematrixfactorization |