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A novel dictionary learning method based on total least squares approach with application in high dimensional biological data

Author

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  • Parvaneh Parvasideh

    (Tarbiat modares university)

  • Mansoor Rezghi

    (Tarbiat modares university)

Abstract

In recent years dictionary learning has become a favorite sparse feature extraction technique. Dictionary learning represents each data as a sparse combination of atoms (columns) of the dictionary matrix. Usually, the input data is contaminated by errors that affect the quality of the obtained dictionary and so sparse features. This effect is especially critical in applications with high dimensional data such as gene expression data. Therefore, some robust dictionary learning methods have investigated. In this study, we proposed a novel robust dictionary learning algorithm, based on the total least squares, that could consider the inexactness of data in modeling. We confirm that standard and some robust dictionary learning models are the particular cases of our proposed model. Also, the results on various data indicate that our method performs better than other dictionary learning methods on high dimensional data.

Suggested Citation

  • Parvaneh Parvasideh & Mansoor Rezghi, 2021. "A novel dictionary learning method based on total least squares approach with application in high dimensional biological data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 575-597, September.
  • Handle: RePEc:spr:advdac:v:15:y:2021:i:3:d:10.1007_s11634-020-00417-4
    DOI: 10.1007/s11634-020-00417-4
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    References listed on IDEAS

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    1. Juliane Sigl, 2016. "Nonlinear residual minimization by iteratively reweighted least squares," Computational Optimization and Applications, Springer, vol. 64(3), pages 755-792, July.
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