Paper
22 October 2024 Partial least squares feature learning via general covariance matrices
Chenyue Pan, Yushi Zhou, Wenyang Wang, Hang Qi, Yunhao Yuan
Author Affiliations +
Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132741Z (2024) https://doi.org/10.1117/12.3038372
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China
Abstract
In this paper, we develop a novel general covariance matrices based partial least squares (GCMPLS) method for learning latent features from high-dimensional datasets. GCMPLS utilizes Gaussian kernel mappings to map the features in the input spaces to higher-dimensional spaces, thereby being able to extract the nonlinear relationship between two-view features. It uses a simply weighted fusion strategy to fuse general covariance matrices and adopts an alternating iteration method to solve. The effectiveness of this method is validated through a series of experiments. The results indicate that GCMPLS outperforms traditional methods on multiple datasets.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Chenyue Pan, Yushi Zhou, Wenyang Wang, Hang Qi, and Yunhao Yuan "Partial least squares feature learning via general covariance matrices", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132741Z (22 October 2024); https://doi.org/10.1117/12.3038372
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KEYWORDS
Covariance matrices

Machine learning

Feature extraction

Data modeling

Image classification

Matrices

Principal component analysis

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