Dec 9, 2020 · In particular, we claim that when classes are determined by linear or non-linear relationships, PLS-DA provides almost no insight into the data.
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Dec 9, 2020 · Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and ...
Our work sheds light on the kind of relationships and data models with which PLS-DA can be effective both as a feature selector as well as a classifier. In ...
PLS-DA is both linear and parametric, and thus may not perform well on data with relationships with are non-linear and non-parametric [40] . RF models are more ...
Our work sheds light on the kind of relationships and data models with which PLS-DA can be effective both as a feature selector as well as a classifier. In ...
Oct 21, 2017 · PLS-DA is able to select the correct hyperplane even with few samples and even when the separation between the clusters is low (values close to ...
Dec 9, 2020 · Background Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a ...
PLS-DA retains a strong performance even when the classes are contained in n-orthotopes (i.e., rectangular boxes in the subspace of the signal features).
Mar 21, 2022 · Broadly speaking I would say PLS-DA should outperform PCA when class information is not distributed with variation in the variables.
Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and ...