Abstract
A few unmeasured factors, otherwise called latent factors, are identified to explain a much larger number of measured factors, e.g., highly expressed chromosome-clustered genes. Unlike factor analysis, partial least squares (PLS) identifies not only exposure (x-value) but also outcome (y-value) variables.
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Cleophas, T.J., Zwinderman, A.H. (2014). Factor Analysis and Partial Least Squares for Complex-Data Reduction (250 Patients). In: Machine Learning in Medicine - Cookbook. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-04181-0_7
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DOI: https://doi.org/10.1007/978-3-319-04181-0_7
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-04181-0
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