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This paper introduces Kronecker coVariance Neural Networks (KVNNs), a temporal graph neural network that decouples spatial and temporal dependencies using Kronecker products to improve spatiotemporal data analysis, addressing limitations of traditional methods like ST-PCA.
The study demonstrates that different representation learning objectives recover distinct latent structures from psychometric data, with contrastive objectives enhancing teacher-child retrieval but PCA-based methods better preserving behavioral phenotype organization.
Proposes PP-CPCANet, a covariance-free framework for domain generalization that learns a global orthogonal basis on the Stiefel manifold and achieves SOTA performance on four benchmarks.
A short mathematical write-up on Principal Component Analysis (PCA), explaining the concept and its applications.