Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints

DOI10.1016/j.neunet.2021.07.023zbMath1521.68203arXiv2011.12659OpenAlexW3186041486MaRDI QIDQ6079088

Johan A. K. Suykens, Francesco Tonin, Panagiotis Patrinos

Publication date: 28 September 2023

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2011.12659

zbMATH Keywords

kernel methodsunsupervised learningmanifold learninglearning disentangled representations

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

Cites Work

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• A feasible method for optimization with orthogonality constraints
• Accuracy of suboptimal solutions to kernel principal component analysis
• Training restricted Boltzmann machines: an introduction
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• Independent component analysis, a new concept?
• A Riemannian conjugate gradient method for optimization on the Stiefel manifold
• Learning Deep Architectures for AI
• Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
• The Geometry of Algorithms with Orthogonality Constraints
• Optimization Techniques on Riemannian Manifolds
• Deep Restricted Kernel Machines Using Conjugate Feature Duality
• A Fast Learning Algorithm for Deep Belief Nets

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