Paper Info
Title
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
Abstract
We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine formulation for kernel PCA by orthogonality constraints on the latent variables to promote disentanglement and to make it possible to carry out optimization without first defining a stabilized objective. After discussing a number of algorithms for end-to-end training, we quantitatively evaluate the proposed method’s effectiveness in disentangled feature learning. We demonstrate on four benchmark datasets that this approach performs similarly overall to β-VAE on several disentanglement metrics when few training points are available while being less sensitive to randomness and hyperparameter selection than β-VAE. We also present a deterministic initialization of Constr-DRKM’s training algorithm that significantly improves the reproducibility of the results. Finally, we empirically evaluate and discuss the role of the number of layers in the proposed methodology, examining the influence of each principal component in every layer and showing that components in lower layers act as local feature detectors capturing the broad trends of the data distribution, while components in deeper layers use the representation learned by previous layers and more accurately reproduce higher-level features.
Year
DOI
Venue
2021
10.1016/j.neunet.2021.07.023
Neural Networks
Keywords
DocType
Volume
Kernel methods,Unsupervised learning,Manifold learning,Learning disentangled representations
Journal
142
Issue
ISSN
Citations 
1
0893-6080
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Francesco Tonin100.34
Panagiotis Patrinos226831.71
J. A. Suykens3305.97