Nonlinear Source Separation Based on Multi-Layer Perceptron: Application on Remote Sensing Analysis

Source separation is relatively a new area of data analysis. The most widely used separation approach's are linear. However, in many realistic cases the process which generates the observations is nonlinear and no information is available about the mixture. In this case, it can be expected to capture the structure of the data better if the data points lie in a nonlinear manifold instead of a linear subspace. In this paper, we try to find a model which allows a compact description of the observations in the hope of discovering some of the underlying causes or sources of the observations. Then, we will process a dimension reduction to classify the obtained sources and evaluate the performances of the proposed method.