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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Bibliographic Details
Published in:IEEE transactions on cognitive and developmental systems Vol. 16; no. 1; p. 1
Main Authors: Einizade, Aref, Sardouie, Sepideh Hajipour
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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 challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem or performing matrix inversion, this paper proposes two PLS-based algorithms, Pseudo-Sparse PLS (PS-PLS) and iterative Pseudo-Sparse Higher-Order PLS (iPS-HOPLS). In these proposed methods, we add the Pseudo-Sparsity term to reduce the L1 norm of the regression coefficient vector in a selective scheme for better importance interpretation while keeping the algorithm as simple as possible. Regarding the evaluation of the proposed methods, we investigate three critical high-dimensionality regression problems of 1) the prediction of 3D trajectory from Electrocorticography (ECoG) recordings, 2) decoding continuous fluctuation of the Electromyography (EMG) powers from recorded Magnetoencephalography (MEG) signals, and 3) continuous decoding of the finger forces from the High-Density surface Electromyogram (HD-sEMG) signals. As well as providing cognitive-relevant interpretations, the experimental results show significant improvements over the generic methods and competitive performance compared to the state-of-the-art regularized PLS approaches.
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ISSN:2379-8920
2379-8939
DOI:10.1109/TCDS.2023.3267010