Dynamic Supervised Principal Component Analysis for Classification

TL;DR

This paper presents a new dynamic supervised dimension reduction method using kernel smoothing to improve classification accuracy and efficiency in evolving high-dimensional data.

Contribution

It introduces a novel adaptive framework for dynamic classification that extends discriminant analysis with kernel smoothing for optimal subspace identification.

Findings

Significant improvement in classification accuracy

Enhanced computational efficiency

Effective handling of evolving class distributions

Abstract

This paper introduces a novel framework for dynamic classification in high dimensional spaces, addressing the evolving nature of class distributions over time or other index variables. Traditional discriminant analysis techniques are adapted to learn dynamic decision rules with respect to the index variable. In particular, we propose and study a new supervised dimension reduction method employing kernel smoothing to identify the optimal subspace, and provide a comprehensive examination of this approach for both linear discriminant analysis and quadratic discriminant analysis. We illustrate the effectiveness of the proposed methods through numerical simulations and real data examples. The results show considerable improvements in classification accuracy and computational efficiency. This work contributes to the field by offering a robust and adaptive solution to the challenges of scalability and non-staticity in high-dimensional data classification.