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PCA-enhanced autoencoders for nonlinear dimensionality reduction in low data regimes

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.21428/594757db.05a13011
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Affiliation
  1. National Research Council of Canada. Digital Technologies
  2. National Research Council of Canada. Advanced Electronics and Photonics
FormatText, Article
ConferenceThe 36th Canadian Conference on Artificial Intelligence (Canadian AI 2023), June 5-9, 2023, Montréal, Québec
Physical description12 p.
Subjectdimensionality reduction; autoencoders; principal component analysis (PCA); limited datasets
Abstract
Many scientific domains, such as nanophotonic design, gene expression, and materials design, are limited by high costs of acquiring data. This data is often intrinsically low-dimensional, nonlinear, and benefits from dimensionality reduction. Autoencoders (AE) provide nonlinear dimensionality reduction but are typically ineffective for low data regimes. Principal Component Analysis (PCA) is data-efficient but limited to linear dimensionality reduction. We propose a technique that harnesses the benefits of both methods by using PCA to initialize an AE. The proposed approach outperforms both PCA and standard AEs in low-data regimes and is comparable to the best of either of the two in other scenarios.
Publication date
PublisherCanadian Artificial Intelligence Association
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  • Proceedings of the 36th Canadian Conference on Artificial Intelligence (5 June 2023).
LanguageEnglish
Peer reviewedYes
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Record identifierddd45128-1b17-43b4-9c55-7fb4dc3c954f
Record created2023-08-23
Record modified2023-08-28
Date modified: