| Téléchargement | - Voir la version finale : PCA-enhanced autoencoders for nonlinear dimensionality reduction in low data regimes (PDF, 2.2 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.21428/594757db.05a13011 |
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| Auteur | Rechercher : Al-Digeil, Muhammad1; Rechercher : Grinberg, Yuri1; Rechercher : Melati, Daniele; Rechercher : Schmid, Jens H.2; Rechercher : Cheben, Pavel2; Rechercher : Janz, Siegfried2; Rechercher : Xu, Dan-Xia2 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
- Conseil national de recherches du Canada. Électronique et photonique avancées
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| Format | Texte, Article |
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| Conférence | The 36th Canadian Conference on Artificial Intelligence (Canadian AI 2023), June 5-9, 2023, Montréal, Québec |
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| Description physique | 12 p. |
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| Sujet | dimensionality reduction; autoencoders; principal component analysis (PCA); limited datasets |
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| Résumé | 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. |
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| Date de publication | 2023-06-05 |
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| Maison d’édition | Canadian Artificial Intelligence Association |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | ddd45128-1b17-43b4-9c55-7fb4dc3c954f |
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| Enregistrement créé | 2023-08-23 |
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| Enregistrement modifié | 2023-08-28 |
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