AI RESEARCH
NeuralFLoC: Neural Flow-Based Joint Registration and Clustering of Functional Data
arXiv CS.LG
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ArXi:2602.03169v2 Announce Type: replace-cross Clustering functional data in the presence of phase variation is challenging, as temporal misalignment can obscure intrinsic shape differences and degrade clustering performance. Most existing approaches treat registration and clustering as separate tasks or rely on restrictive parametric assumptions. We present \textbf{NeuralFLoC}, a fully unsupervised, end-to-end deep learning framework for joint functional registration and clustering based on Neural ODE-driven diffeomorphic flows and spectral clustering.