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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.