#5794. Lifting the limitations of Gaussian mixture regression through coupling with principal component analysis and deep autoencoding

July 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Spectroscopy;
Analytical Chemistry;
Computer Science Applications;
Software;
Process Chemistry and Technology;
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Abstract:
The mathematical modeling of correlations between target properties and their factors of influence, particularly that allowing inverse analysis, is an essential part of molecular, material, and process designs. In contrast to approaches employing pseudo-inverse analysis, Gaussian mixture regression (GMR), which assumes that the relationships between variables can be represented as a mixture of Gaussian distributions, allows for direct inverse analysis. However, as this model optimizes the means and variance–covariance matrices of all variables, parameter estimation becomes increasingly difficult with the increasing number of variables. Herein, this drawback is addressed by the transformation of explanatory variables X into latent variables Z before GMR modeling. As the inverse (Z to X) transformation is necessary for direct inverse analysis, principal component analysis (PCA) and deep autoencoding (DAE) are employed as dimensionality reduction methods.
Keywords:
DAE; Deep autoencoding; Direct inverse analysis; Gaussian mixture modeling; Gaussian mixture regression; Generative topographic mapping regression; GMM; GMR; GTMR; Material design Gaussian mixture regression; PCA; Principal component analysis; RMSE; Root-mean-squared error

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