Dimensionality Reduction and Reconstruction of Data Based on Autoencoder Network

The curse of dimensionality is a central difficulty in many fields such as machine learning, pattern recognition and data mining etc. The dimensionality reduction method of characteristic data is one of the current research hotspots in data-driven calculation methods, which high-dimensional data is mapped into a low-dimensional space. In this paper, a special nonlinear dimensionality reduction method called Autoencoder is introduced, which uses Continuous Restricted Boltzmann Machine (CRBM) and converts high-dimensional data to low-dimensional codes by training a neural network with multiple hidden layers. In particular, the autoencoder provides such a bi-directional mapping between the high-dimensional data space and the low-dimensional manifold space and is therefore able to overcome the inherited deficiency of most nonlinear dimensionality reduction methods that do not have an inverse mapping. The experiments on synthetic datasets and true image data show that the 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.