Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction
Autoencoders are widely used for nonlinear dimensionality reduction and manifold learning. While most common implementations rely on both nonlinear encoders and decoders, we investigate the specific role of the encoder and the extent to which it can be constrained to be linear without reducing accuracy. We conduct a comparative study on four autoencoder architectures: standard fully nonlinear autoencoders (AE), linear-encoder autoencoders (Lenc-AE), linear-decoder autoencoders (Ldec-AE), and fully linear autoencoders (LAE), evaluated on synthetic manifolds, computational mechanics data sets, and real-world image data sets including MNIST. We demonstrate that imposing a linear encoder pres...
| Authors | Louen Pottier, Louis Lesueur, Anders Thorin |
|---|---|
| Primary category | cs.LG |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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