Iterative Pseudo-Sparse Partial Least Square and its Higher-Order variant: Application to inference from high-dimensional biosignals

Partial Least Square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe chal...

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Vydané v:	IEEE transactions on cognitive and developmental systems Ročník 16; číslo 1; s. 1
Hlavní autori:	Einizade, Aref, Sardouie, Sepideh Hajipour
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Feature extraction Fingers Iterative algorithms Iterative methods iterative Pseudo-Sparse Higher-Order PLS (iPS-HOPLS) Least squares Linear regression Machine Learning Magnetoencephalography Optimization Partial Least Square (PLS) Prediction algorithms Pseudo-Sparse PLS (PS-PLS) Regression Regression coefficients Task analysis Tensors
ISSN:	2379-8920, 2379-8939
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