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Fast semi-supervised discriminant analysis for binary classification of large data sets
Journal Contribution - Journal Article
© 2019 Elsevier Ltd High-dimensional data requires scalable algorithms. We propose and analyze four scalable and related algorithms for semi-supervised discriminant analysis (SDA). These methods are based on Krylov subspace methods and therefore exploit data sparsity and the shift-invariance of Krylov subspaces. In addition, centralization was derived for the semi-supervised setting. The proposed methods are evaluated on an industry-scale data set from a pharmaceutical company to predict compound activity on target proteins. The results show that our methods only require a few seconds, significantly improving computation time on the state of the art.
Journal: Pattern Recognition
Pages: 86 - 99