Fun with Flags: Robust Principal Directions via Flag Manifolds (2401.04071v4)

Abstract: Principal component analysis (PCA), along with its extensions to manifolds and outlier contaminated data, have been indispensable in computer vision and machine learning. In this work, we present a unifying formalism for PCA and its variants, and introduce a framework based on the flags of linear subspaces, ie a hierarchy of nested linear subspaces of increasing dimension, which not only allows for a common implementation but also yields novel variants, not explored previously. We begin by generalizing traditional PCA methods that either maximize variance or minimize reconstruction error. We expand these interpretations to develop a wide array of new dimensionality reduction algorithms by accounting for outliers and the data manifold. To devise a common computational approach, we recast robust and dual forms of PCA as optimization problems on flag manifolds. We then integrate tangent space approximations of principal geodesic analysis (tangent-PCA) into this flag-based framework, creating novel robust and dual geodesic PCA variations. The remarkable flexibility offered by the 'flagification' introduced here enables even more algorithmic variants identified by specific flag types. Last but not least, we propose an effective convergent solver for these flag-formulations employing the Stiefel manifold. Our empirical results on both real-world and synthetic scenarios, demonstrate the superiority of our novel algorithms, especially in terms of robustness to outliers on manifolds.

• The paper recasts robust and dual PCA as optimization problems on flag manifolds to unify various PCA techniques.
• It introduces a framework that leverages nested subspaces and tangent geodesic analysis to handle anomalies in data.
• A convergent Riemannian optimization algorithm on the Stiefel manifold is proposed and validated on both real and synthetic datasets.

Overview of Flag Manifolds in Dimensionality Reduction

Flag manifolds provide a fascinating avenue for exploring principal component analysis (PCA) and its variants. Often encapsulated within larger dimensions, the nested linear subspaces labeled as 'flags' impart unique hierarchical relationships that are crucial to understanding complex data patterning. This framework keeps a tab on the core subspace interactions—tracing how each subspace contributes to the collective data narrative.

A Unified Framework

A novel accomplishment in dimensionality reduction has been achieved by recasting robust PCA and dual PCA as optimization problems on flag manifolds. These abstract mathematical spaces encapsulate the concept of flags—nested sequences of subspaces—allowing us to merge a wide array of PCA techniques into a single framework. Here, traditional PCA methods, which typically focus on variance maximization or reconstruction error minimization, are melded into a versatile platform able to handle anomalies in data or manifold-based structures.

Extending Variants

The reach of this new framework is broad, encompassing everything from outlier-sensitive dual PCA to tangent versions of principal geodesic analysis. Tangent spaces, part of the manifold-based analysis, are used to develop these novel robust and dual geodesic PCA variations. Encouragingly, this flexibility extends further, yielding more algorithmic variants tailored by specific flag types which classify how the data may be nested within the subspaces.

The Power of Riemannian Optimization

A single, convergent solving algorithm is proposed, which is adept at managing these flag-formulated optimization cases efficiently, by performing Riemannian optimization on the Stiefel manifold—a concept of great interest in the optimization arena. Its significance is not only in offering a common computational platform but also proving convergence for dual PCA. This depicts a considerable stride, considering the open challenges associated with direct optimization on flag manifolds.

Empirical Validation

Real-world data scenarios, alongside synthetic datasets, have served as testing grounds for these novel algorithms, demonstrating an exceptional robustness against outlier contamination on manifolds. The exploration of this principle has led to a breakthrough in optimization techniques that find applications ranging from outlier prediction to shape analysis, showcasing the foundational role of PCA in capturing data variation with fewer dimensions.

Conclusion

In conclusion, the intricate paper of flags within the PCA domain has resulted in a groundbreaking unification of PCA techniques. This unification, in turn, showcases the robustness and adaptability of these algorithms in capturing intricate data structures, including manifold-based relationships. The results of this research effort hold significant implications for future advancements in both theoretical exploration and practical applications of PCA.