Algorithms for Regularized Linear Discriminant Analysis

0439199 - ÚI 2015 RIV PT eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Duintjer Tebbens, Jurjen
Algorithms for Regularized Linear Discriminant Analysis.
BIOINFORMATICS 2015. Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. Lisbon: Scitepress, 2015, s. 128-133. ISBN 978-989-758-070-3.
[BIOINFORMATICS 2015. International Conference on Bioinformatics Models, Methods and Algorithms. Lisbon (PT), 12.01.2015-15.01.2015]
Grant CEP: GA ČR GA13-17187S; GA ČR GA13-06684S
Institucionální podpora: RVO:67985807
Klíčová slova: classification analysis * regularization * high-dimensional data * decomposition * computational aspects
Kód oboru RIV: IN - Informatika

This paper is focused on regularized versions of classification analysis and their computation for highdimensional data. A variety of regularized classification methods has been proposed and we critically discuss their computational aspects. We formulate several new algorithms for regularized linear discriminant analysis, which exploits a regularized covariance matrix estimator towards a regular target matrix. Numerical linear algebra considerations are used to propose tailor-made algorithms for specific choices of the target matrix. Further, we arrive at proposing a new classification method based on L2-regularization of group means and the pooled covariance matrix and accompany it by an efficient algorithm for its computation.
Trvalý link: http://hdl.handle.net/11104/0242575