🏢 DI ENS, CRNS, PSL University, INRIA Paris
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
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Machine Learning
Unsupervised Learning
🏢 DI ENS, CRNS, PSL University, INRIA Paris
This paper presents novel informational results and a new algorithm (‘Ping-Pong’) for solving the Procrustes-Wasserstein problem, significantly advancing high-dimensional data alignment.