A Computational Perspective on Projection Pursuit in High Dimensions: Feasible or Infeasible Feature Extraction. (19th August 2022)

• Record Type: Journal Article
• Authors: Zhang, Chunming; Ye, Jimin; Wang, Xiaomei
• Is Part Of: International statistical review. Volume 91:Number 1(2023)
• Page Start: 140
• Page End: 161
• Publication Date: 2022-08-19
• Subjects: density estimation -- empirical distribution function -- exploratory data analysis -- Gaussian mixture -- ICA -- PCA; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12517
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: Finding a suitable representation of multivariate data is fundamental in many scientific disciplines. Projection pursuit ( PP ) aims to extract interesting 'non‐Gaussian' features from multivariate data, and tends to be computationally intensive even when applied to data of low dimension. In high‐dimensional settings, a recent work (Bickel et al., 2018) on PP addresses asymptotic characterization and conjectures of the feasible projections as the dimension grows with sample size. To gain practical utility of and learn theoretical insights into PP in an integral way, data analytic tools needed to evaluate the behaviour of PP in high dimensions become increasingly desirable but are less explored in the literature. This paper focuses on developing computationally fast and effective approaches central to finite sample studies for (i) visualizing the feasibility of PP in extracting features from high‐dimensional data, as compared with alternative methods like PCA and ICA, and (ii) assessing the plausibility of PP in cases where asymptotic studies are lacking or unavailable, with the goal of better understanding the practicality, limitation and challenge of PP in the analysis of large data sets.