So you think you can PLS-DA?

Background Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and c...

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Bibliographic Details
Published in:	BMC bioinformatics 2020-12, Vol.21 (Suppl 1), p.2-10, Article 2
Main Authors:	Ruiz-Perez, Daniel, Guan, Haibin, Madhivanan, Purnima, Mathee, Kalai, Narasimhan, Giri
Format:	Article
Language:	English
Subjects:	Algorithms Bioinformatics Biomedical and Life Sciences Computational Biology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Datasets Dimensionality reduction Discriminant Analysis Experiments Factor analysis Feature selection Ground truth Labels Learning algorithms Least squares Least-Squares Analysis Life Sciences Machine Learning Methods Microarrays Microbiomes Noise PCA PLS-DA Principal Component Analysis Principal components analysis Signal to noise ratio Sparsity Vagina
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