DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification

• URL: http://arxiv.org/abs/2501.18116v3
• Date: Sat, 27 Sep 2025 09:04:34 GMT
• Title: DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification
• Authors: Siyuan Jiang, Yihan Hu, Wenjie Li, Pengcheng Zeng,
• Abstract summary: DeepFRC is an end-to-end deep learning framework that jointly learns warping functions and a classification within a unified architecture.<n>We provide the first theoretical guarantees for such a joint model, proving its ability to approximate optimal warpings.<n>Experiments on synthetic and real-world datasets demonstrate that DeepFRC consistently outperforms state-of-the-art methods in both alignment quality and classification accuracy.
• Score: 11.194964108429252
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Functional data, representing curves or trajectories, are ubiquitous in fields like biomedicine and motion analysis. A fundamental challenge is phase variability -- temporal misalignments that obscure underlying patterns and degrade model performance. Current methods often address registration (alignment) and classification as separate, sequential tasks. This paper introduces DeepFRC, an end-to-end deep learning framework that jointly learns diffeomorphic warping functions and a classifier within a unified architecture. DeepFRC combines a neural deformation operator for elastic alignment, a spectral representation using Fourier basis for smooth functional embedding, and a class-aware contrastive loss that promotes both intra-class coherence and inter-class separation. We provide the first theoretical guarantees for such a joint model, proving its ability to approximate optimal warpings and establishing a data-dependent generalization bound that formally links registration fidelity to classification performance. Extensive experiments on synthetic and real-world datasets demonstrate that DeepFRC consistently outperforms state-of-the-art methods in both alignment quality and classification accuracy, while ablation studies validate the synergy of its components. DeepFRC also shows notable robustness to noise, missing data, and varying dataset scales. Code is available at https://github.com/Drivergo-93589/DeepFRC.

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