A multiple hold-out framework for Sparse Partial Least Squares
نویسندگان
چکیده
منابع مشابه
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 mechanisms by finding relationships between neuroimaging and clinical/demographic data. The identificat...
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ژورنال
عنوان ژورنال: Journal of Neuroscience Methods
سال: 2016
ISSN: 0165-0270
DOI: 10.1016/j.jneumeth.2016.06.011