A New Algorithm for Exploratory Projection Pursuit

TL;DR

This paper introduces a flexible new algorithm for exploratory projection pursuit that adapts interestingness measures to specific data and problem contexts, enhancing the detection of subtle and complex structures in multivariate data.

Contribution

The paper presents a novel algorithm that allows data-dependent interestingness definitions and introduces projection indices based on the spatial distribution function, broadening applicability.

Findings

Effectively detects subtle data structures

Adapts to various data types and problems

Demonstrates success on real datasets

Abstract

In this paper, we propose a new algorithm for exploratory projection pursuit. The basis of the algorithm is the insight that previous approaches used fairly narrow definitions of interestingness / non interestingness. We argue that allowing these definitions to depend on the problem / data at hand is a more natural approach in an exploratory technique. This also allows our technique much greater applicability than the approaches extant in the literature. Complementing this insight, we propose a class of projection indices based on the spatial distribution function that can make use of such information. Finally, with the help of real datasets, we demonstrate how a range of multivariate exploratory tasks can be addressed with our algorithm. The examples further demonstrate that the proposed indices are quite capable of focussing on the interesting structure in the data, even when this structure is otherwise hard to detect or arises from very subtle patterns.