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A Compressive Sensing Model for Speeding Up Text Classification
Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428956/ https://www.ncbi.nlm.nih.gov/pubmed/32831821 http://dx.doi.org/10.1155/2020/8879795 |
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author | Shen, Kelin Hao, Peinan Li, Ran |
author_facet | Shen, Kelin Hao, Peinan Li, Ran |
author_sort | Shen, Kelin |
collection | PubMed |
description | Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model has a low time and space complexity while training a text classifier, and the restricted isometry property (RIP) of CS ensures that pairwise distances between text features can be well preserved in the process of dimensionality reduction. In particular, by structural random matrices (SRMs), CS is free from computation and memory limitations in the construction of random projections. Experimental results demonstrate that CS effectively accelerates the text classification while hardly causing any accuracy loss. |
format | Online Article Text |
id | pubmed-7428956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-74289562020-08-20 A Compressive Sensing Model for Speeding Up Text Classification Shen, Kelin Hao, Peinan Li, Ran Comput Intell Neurosci Research Article Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model has a low time and space complexity while training a text classifier, and the restricted isometry property (RIP) of CS ensures that pairwise distances between text features can be well preserved in the process of dimensionality reduction. In particular, by structural random matrices (SRMs), CS is free from computation and memory limitations in the construction of random projections. Experimental results demonstrate that CS effectively accelerates the text classification while hardly causing any accuracy loss. Hindawi 2020-08-07 /pmc/articles/PMC7428956/ /pubmed/32831821 http://dx.doi.org/10.1155/2020/8879795 Text en Copyright © 2020 Kelin Shen et al. http://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 Shen, Kelin Hao, Peinan Li, Ran A Compressive Sensing Model for Speeding Up Text Classification |
title | A Compressive Sensing Model for Speeding Up Text Classification |
title_full | A Compressive Sensing Model for Speeding Up Text Classification |
title_fullStr | A Compressive Sensing Model for Speeding Up Text Classification |
title_full_unstemmed | A Compressive Sensing Model for Speeding Up Text Classification |
title_short | A Compressive Sensing Model for Speeding Up Text Classification |
title_sort | compressive sensing model for speeding up text classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428956/ https://www.ncbi.nlm.nih.gov/pubmed/32831821 http://dx.doi.org/10.1155/2020/8879795 |
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