Functional Autoencoders for Amplitude-Phase Representation Learning
Functional data are intrinsically infinite-dimensional, and often exhibit phase variation, where corresponding events occur at different times across observations. Existing linear dimension reduction methods struggle with nonlinear amplitude variation, while functional autoencoders without an explicit warp entangle temporal misalignment with shape. We propose the Amplitude--Phase Functional Autoencoders (AP-FAE), an unsupervised framework for functional data that spans both univariate and multivariate cases, with emphasis on the multivariate setting, and factorizes the latent space into separate amplitude and phase embeddings derived from all channels. A smooth functional decoder reconstr...
| Authors | Peida Wu, Xinyang Xiong, Pengcheng Zeng |
|---|---|
| Primary category | stat.ML |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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