Sparse Estimation of the Precision Matrix and Plug-In Principle in Linear Discriminant Analysis for Hyperspectral Image Classification

Authors

  • M. L. Picco Universidad Nacional de Rio Cuarto
  • M. S. Ruiz

DOI:

https://doi.org/10.5540/tcam.2022.023.03.00595

Abstract

In this paper, a new method for supervised classification of hyperspectral images is proposed for the case in which the size of the training sample is small. It consists of replacing  in the Mahalanobis  distance the maximum likelihood estimator of the precision matrix   by a  sparse estimator. The method is compared with two other existing versions of \textit{LDA} sparse, both in real and simulated images.

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Published

2022-09-12

How to Cite

Picco, M. L., & Ruiz, M. S. (2022). Sparse Estimation of the Precision Matrix and Plug-In Principle in Linear Discriminant Analysis for Hyperspectral Image Classification. Trends in Computational and Applied Mathematics, 23(3), 595–605. https://doi.org/10.5540/tcam.2022.023.03.00595

Issue

Vol. 23 No. 3 (2022)

Section

Original Article

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