Manifold learning about novel continuous autoencoder network
WU You-lin · Computer Engineering and Applications Journal · 2009
The main goal of manifold learning is to find a low-dimensional manifold embedded in high-dimensional data space. Based on previous nonlinear dimensionality reduction methods using neural network,a novel Continuous Autoencoder (C-Au- toencoder) network is put forward in this paper.The method specially uses Continuous Restricted Boltzmann Machine(CRBM) and coverts high-dimensional data to low-dimensional codes by training a neural network with multiple hidden layers,and vice versa reconstructs original high-dimensional data.In particular,the C-Autoencoder provides such a bi-directional mapping between the high-dimensional data space and the low-dimensional manifold space and is not only able to overcome the inherited deficiency of most nonlinear dimensionality reduction methods that do not have an inverse mapping but also especially suitable for dimensionality reduction and reconstruction of high-dimensional continuous data.The experiments on synthetic datasets show that the C-Autoencoder network not only can find the embedded manifold of high-dimensional datasets but also reconstruct exactly the original high-dimension datasets from low-dimensional structure.