most citedPCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes

2 citations · 2 across the 1 of their papers we have counts for

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cs.LG2022★ 2 cited

PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes

Muhammad Al-Digeil, Yuri Grinberg, Daniele Melati3 +5

Autoencoders (AE) provide a useful method for nonlinear dimensionality reduction but are ill-suited for low data regimes. Conversely, Principal Component Analysis (PCA) is data-eff…