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Open AccessArticle

Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders

by 1,†, 2 and 1,*,†
1
School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW 2308, Australia
2
4Tel Pty. Ltd., Warabrook, NSW 2304, Australia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Algorithms 2019, 12(9), 186; https://doi.org/10.3390/a12090186
Received: 20 June 2019 / Revised: 25 August 2019 / Accepted: 3 September 2019 / Published: 6 September 2019
The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations of the same underlying manifold. Manifold alignment can be successful in detecting latent manifolds in cases where one version of the data alone is not sufficient to extract and establish a stable low-dimensional representation. The present study proposes a parallel deep autoencoder neural network architecture for manifold alignment and conducts a series of experiments using a protein-folding benchmark dataset and a suite of new datasets generated by simulating double-pendulum dynamics with underlying manifolds of dimensions 2, 3 and 4. The dimensionality and topological complexity of these latent manifolds are above those occurring in most previous studies. Our experimental results demonstrate that the parallel deep autoencoder performs in most cases better than the tested traditional methods of semi-supervised manifold alignment. We also show that the parallel deep autoencoder can process datasets of different input domains by aligning the manifolds extracted from kinematics parameters with those obtained from corresponding image data. View Full-Text
Keywords: deep autoencoder; dimensionality reduction; manifold learning; 3-manifold; machine learning; manifold alignment; autoencoder; deep neural network; deep learning; double pendulum deep autoencoder; dimensionality reduction; manifold learning; 3-manifold; machine learning; manifold alignment; autoencoder; deep neural network; deep learning; double pendulum
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Aziz, F.; Wong, A.S.W.; Chalup, S. Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders. Algorithms 2019, 12, 186.

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