A Fast Iterative Robust Principal Component Analysis Method

TL;DR

This paper introduces a fast iterative robust PCA method that efficiently estimates data inliers to improve robustness against outliers, maintaining accuracy while reducing computational costs.

Contribution

The paper presents a novel FIR PCA approach that leverages Incremental PCA for improved robustness and efficiency in outlier-rich data environments.

Findings

Achieves competitive accuracy with existing robust PCA methods

Demonstrates improved robustness to outliers

Effective on both simulated and real-world datasets

Abstract

Principal Component Analysis (PCA) is widely used for dimensionality reduction and data analysis. However, PCA results are adversely affected by outliers often observed in real-world data. Existing robust PCA methods are often computationally expensive or exhibit limited robustness. In this work, we introduce a Fast Iterative Robust (FIR) PCA method by efficiently estimating the inliers center location and covariance. Our approach leverages Incremental PCA (IPCA) to iteratively construct a subset of data points that ensures improved location and covariance estimation that effectively mitigates the influence of outliers on PCA projection. We demonstrate that our method achieves competitive accuracy and performance compared to existing robust location and covariance methods while offering improved robustness to outlier contamination. We utilize simulated and real-world datasets to evaluate and demonstrate the efficacy of our approach in identifying and preserving underlying data structures in the presence of contamination.