Using Neural Networks for Dual Dimensionality Reduction

When studying different biological or other complex systems, one often needs to identify correlated features of observable of the system, later to be included in the system’s models. However, those observables are often multidimensional with many features contributing to the correlation. It is then the job of data analysis to identify the smallest subset of features of the variables that encapsulates such correlations. Here we develop a deep learning based method for performing a dual dimensionality reduction: compressing two multidimensional variables while maximizing the correlation between their compressed description. The method can detect nonlinear statistical dependencies among the variables.