Correlation Based Principal Loading Analysis

TL;DR

This paper enhances principal loading analysis by integrating correlation and covariance matrices, using rescaled eigenvectors, and updating algorithms to improve dimension reduction techniques.

Contribution

It introduces a combined correlation-covariance approach with rescaled eigenvectors and provides updated algorithms for principal loading analysis.

Findings

Improved dimension reduction by combining correlation and covariance matrices.

Rescaled eigenvectors enhance the interpretability of principal loadings.

Updated algorithms facilitate implementation of the proposed method.

Abstract

Principal loading analysis is a dimension reduction method that discards variables which have only a small distorting effect on the covariance matrix. We complement principal loading analysis and propose to rather use a mix of both, the correlation and covariance matrix instead. Further, we suggest to use rescaled eigenvectors and provide updated algorithms for all proposed changes.