A performance evaluation of integrating machine learning schemes utilizing fluidic lenses
Graciana Puentes
TL;DR
The paper tackles how to interpret coarse optical data described by Zernike coefficients from fluidic lenses, proposing a hybrid framework that blends ML and traditional statistics. It demonstrates dimensionality reduction and clustering quality using PCA (two components explaining $95\%$ of variance, with $PC_1$ contributing ~ $70\%$) and FA ($m=3$ factors with a $0.005$ tolerance), complemented by HC that yields a cophenetic coefficient of $c=0.9629$. The study also employs Boxplots and XBAR charts to assess variability and correlations among Zernike terms. Overall, the integrated ML-statistical approach provides a foundation for state-of-the-art analysis and potential improvements in predictive accuracy across methods when applied to normalized Zernike data.
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.