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A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction
Big data has the traits such as “the curse of dimensionality,” high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546659/ https://www.ncbi.nlm.nih.gov/pubmed/36211002 http://dx.doi.org/10.1155/2022/2551137 |
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author | Qiu, Hong Wang, Renfang Sun, Dechao Liu, Xinwei Zhang, Liang Liu, Yunpeng |
author_facet | Qiu, Hong Wang, Renfang Sun, Dechao Liu, Xinwei Zhang, Liang Liu, Yunpeng |
author_sort | Qiu, Hong |
collection | PubMed |
description | Big data has the traits such as “the curse of dimensionality,” high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods. |
format | Online Article Text |
id | pubmed-9546659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95466592022-10-08 A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction Qiu, Hong Wang, Renfang Sun, Dechao Liu, Xinwei Zhang, Liang Liu, Yunpeng Comput Intell Neurosci Research Article Big data has the traits such as “the curse of dimensionality,” high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods. Hindawi 2022-09-30 /pmc/articles/PMC9546659/ /pubmed/36211002 http://dx.doi.org/10.1155/2022/2551137 Text en Copyright © 2022 Hong Qiu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Qiu, Hong Wang, Renfang Sun, Dechao Liu, Xinwei Zhang, Liang Liu, Yunpeng A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction |
title | A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction |
title_full | A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction |
title_fullStr | A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction |
title_full_unstemmed | A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction |
title_short | A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction |
title_sort | smoothed matrix multivariate elliptical distribution-based projection method for feature extraction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546659/ https://www.ncbi.nlm.nih.gov/pubmed/36211002 http://dx.doi.org/10.1155/2022/2551137 |
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