An optimized dimensionality reduction model for high-dimensional data based on Restricted Boltzmann Machines

For high-dimensional data analysis, dimensionality reducing is a common optimization means. A number of traditional multivariate statistical based approaches are applied and proposed recently, but cannot be solving dimensionality reduction problem well. The difficulty is caused by the fact that high-dimensional data generally do not have specific distribution or enough prior information. Aiming at the problem, an optimized dimensionality reduction model based on Restricted Boltzmann Machines (RBM) is presented. The model was optimized through adjusting the RBM hidden layer structure dynamically. Data distribution and prior information are not required in this model. Tests revealed the model performed well for handwritten digits data (get from the MNIST datasets) dimensionality reduction.