Fast semi-supervised discriminant analysis for binary classification of large data-sets

High-dimensional data requires scalable algorithms. We propose and analyze three scalable and related algorithms for semi-supervised discriminant analysis (SDA). These methods are based on Krylov subspace methods which exploit the data sparsity and the shift-invariance of Krylov subspaces. In additi...

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Veröffentlicht in:	arXiv.org 2018-03
Hauptverfasser:	Tavernier, Joris, Simm, Jaak, Meerbergen, Karl, Wegner, Joerg Kurt, Ceulemans, Hugo, Moreau, Yves
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computer Science - Artificial Intelligence Computer Science - Numerical Analysis Computer Science - Performance Discriminant analysis Performance prediction Proteins State of the art Subspace methods Subspaces
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