Integrating machine learning and statistical methods for optical aberration analysis in fluidic lenses

A combination of statistical inference and machine learning (ML) schemes has been utilized to create a thorough understanding of coarse experimental data based on Zernike variables characterizing optical aberrations in fluidic lenses. A classification of surplus-response variables through tolerance manipulation was included to unravel the dimensional aspect of the data. Similarly, the impact of the exclusion of supererogatory variables through the identification of clustering movements of constituents is examined. The method of constructing a spectrum of collaborative results through the application of similar techniques has been tested. To evaluate the suitability of each statistical method before its application on a large dataset, a selection of ML schemes has been proposed. The unsupervised learning tools principal component analysis (PCA), factor analysis (FA), and hierarchical clustering (HC) were employed to define the elemental characteristics of Zernike variables. PCA enabled to reduce the dimensionality of the system by identifying two principal components which collectively account for 95% of the total variance. The execution of FA indicated that a specific tolerance of independent variability of 0.005 could be used to reduce the dimensionality of the system without losing essential data information. A high cophenetic coefficient value of c = 0.9629 validated an accurate clustering division of variables with similar characteristics. The current approach of mutually validating ML and statistical analysis methods will aid in laying the groundwork for advanced analysis. Its benefit is reflected in the associated state-of-the-art (SOTA) framework, which enhances predictive performance by integrating multiple complementary methods rather than relying on a single, arbitrarily selected ML model, as is in conventional SOTA analyses.