SOURCE-LINKED INTELLIGENCE

Robust Multi-Task Learning for Principal Component Analysis

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Principal component analysis (PCA) is a fundamental tool for learning low-dimensional structure from high-dimensional data. When data are collected from multiple sources, the underlying task distributions may exhibit unknown degrees of similarity, with some tasks potentially arising from arbitrary distributions. We propose new multi-task PCA procedures that exploit similarity structure across tasks to improve eigenspace estimation while remaining robust to outlier tasks. We establish non-asymptotic convergence rates and show that the proposed procedures attain minimax optimal rates in a range

Read original source ↗ Open in workspace

recordType

paper

region

Global