A multiple hold-out framework for Sparse Partial Least Squares

BACKGROUND: Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain's...

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Bibliographic Details
Published in:J Neurosci Methods
Main Authors: Monteiro, João M., Rao, Anil, Shawe-Taylor, John, Mourão-Miranda, Janaina
Format: Artigo
Language:Inglês
Published: Elsevier/North-Holland Biomedical Press 2016
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Article
Online Access:https://ncbi.nlm.nih.gov/pmc/articles/PMC5012894/
https://ncbi.nlm.nih.gov/pubmed/27353722
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jneumeth.2016.06.011
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