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An Unsupervised Domain Adaptation Regression Method in Kernel Partial Least Squares Subspace Embedded with Joint Statistical and Manifold Alignment for Fourier-Transform Infrared Spectroscopy in Agri-Food Analysis

https://doi.org/10.2139/ssrn.5027663
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Penalized eigendecompositions: motivations from domain adaptation for calibration transfer
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Examining the interplay between artificial intelligence and the agri-food industry
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Standard-free calibration transfer - An evaluation of different techniques
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Domain adaptive partial least squares regression
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Infrared spectroscopy: an underexploited analytical tool for assessing physicochemical properties of food products and processing
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Boosting the generalization ability of Vis-NIR-spectroscopy-based regression models through dimension reduction and transfer learning
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Implementation of Quality by Design for processing of food products and biotherapeutics
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Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements
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Artificial intelligence applications in the agrifood sectors
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Manifold embedded joint geometrical and statistical alignment for visual domain adaptation
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Domain adaptation with geometrical preservation and distribution alignment
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A brief note on application of domain-invariant PLS for adapting near-infrared spectroscopy calibrations between different physical forms of samples
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Are standard sample measurements still needed to transfer multivariate calibration models between near-infrared spectrometers? The answer is not always
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Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues
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PLS-regression: a basic tool of chemometrics
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Domain-Invariant Partial-Least-Squares Regression
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Standardization of complex biologically derived spectrochemical datasets
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Computer Aided Design of Experiments
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Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
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DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
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Unsupervised Domain Adaptation by Domain Invariant Projection
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Unsupervised Visual Domain Adaptation Using Subspace Alignment
inferredcites →https://doi.org/10.1109/tcyb.2016.2633306openalex-snapshot
Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization
inferredcites →https://doi.org/10.1109/tnn.2010.2091281openalex-snapshot
Domain Adaptation via Transfer Component Analysis
inferredcites →https://doi.org/10.1109/tnnls.2022.3183326openalex-snapshot
Transfer Adaptation Learning: A Decade Survey
inferredcites →https://doi.org/10.1109/tpami.2019.2945942openalex-snapshot
A Review of Domain Adaptation without Target Labels
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Large margin transductive transfer learning
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A Survey of Unsupervised Deep Domain Adaptation
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A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy
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Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives
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Return of Frustratingly Easy Domain Adaptation
inferredcites →https://doi.org/10.48550/arxiv.1206.4683openalex-snapshot
Marginalized Denoising Autoencoders for Domain Adaptation
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Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
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Kernel partial least squares regression in reproducing kernel hilbert space
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