Principal nested spheres for high-dimensional data

TL;DR

This paper introduces an improved, faster Principal Nested Spheres (PNS) method for high-dimensional spherical data, along with new model selection techniques and a visual PNS biplot for variable analysis, demonstrated on cancer datasets.

Contribution

It presents a fast PNS algorithm using PCA, new model selection tests, and a PNS biplot for variable interpretation, enhancing high-dimensional spherical data analysis.

Findings

Faster PNS method suitable for high-dimensional data

Effective variable selection demonstrated on cancer datasets

New visual tool (PNS biplot) for interpreting PNS results

Abstract

The method of Principal Nested Spheres (PNS) is a non-linear dimension reduction technique for spherical data. The method is a backwards fitting procedure, starting with fitting a high-dimensional sphere and then successively reducing dimension at each stage. After reviewing the PNS method in detail, we introduce some new methods for model selection at each stage between great and small subspheres, based on the Kolmogorov-Smirnov test, a variance test and a likelihood ratio test. The current PNS fitting method is slow for high-dimensional spherical data, and so we introduce a fast PNS method which involves an initial principal components analysis decomposition to select a basis for lower dimensional PNS. A new visual method called the PNS biplot is introduced for examining the effects of the original variables on the PNS, and this involves procedures for back-fitting from the PNS scores back to the original variables. The methodology is illustrated with two high-dimensional datasets from cancer research: Melanoma proteomics data with 500 variables and 205 patients, and a Pan Cancer dataset with 12,478 genes and 300 patients. In both applications the PNS biplot is used to select variables for effective classification.