Projection Pursuit CPCANet for Domain Generalization

Yu-Hsi Chen, Abd-Krim Seghouane

Projection Pursuit CPCANet for Domain Generalization: 0 upvotes on Hugging Face Daily Papers, #22 of 29 papers on 2026-07-29. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

Domain Generalization (DG) aims to learn representations robust to distribution shifts. Recent geometric alignment methods, such as CPCANet, extract domain-invariant structures through batch-wise Common Principal Component Analysis (CPCA). However, CPCANet suffers from rank-deficient covariance estimation due to the small-sample-size issue in mini-batch training. To address this limitation, we propose Projection Pursuit CPCANet (PP-CPCANet), a covariance-free framework that learns a global orthogonal basis on the Stiefel manifold and jointly optimizes it with network parameters via the Cayley transform. We further introduce a symmetry-breaking detached-median PP dispersion objective to extract common principal components (CPCs) with dense and robust optimization signals. Experiments on four DG benchmarks show that PP-CPCANet achieves SOTA performance while maintaining stable training.

Paper page on Hugging Face · arXiv

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