A performance evaluation of integrating machine learning schemes utilizing fluidic lenses

TL;DR

This paper evaluates the integration of machine learning and statistical methods to analyze optical aberration data in fluidic lenses, demonstrating dimensionality reduction and clustering techniques to improve understanding and predictive accuracy.

Contribution

It introduces a combined approach of ML and statistical analysis for optical data, validating methods for better dimensionality reduction and clustering in fluidic lens aberration analysis.

Findings

PCA identified two components explaining 95% variance.

FA showed tolerance of 0.005 preserves data integrity.

Hierarchical clustering achieved high cophenetic coefficient of 0.9629.

Abstract

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 supervised 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 foundation for state-of-the-art (SOTA) analysis. The benefit of our approach can be assessed by considering that the associated SOTA will enhance the predictive accuracy between two comparable methods, in contrast to the SOTA analysis conducted between two arbitrary ML methods.