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Stochastic configuration networks (SCNs) as a class of randomized learnermodel have been successfully employed in data analytics due to its universalapproximation capability and fast modelling property. The technical essencelies in stochastically configuring hidden nodes (or basis functions) based on asupervisory mechanism rather than data-independent randomization as usuallyadopted for building randomized neural networks. Given image data modellingtasks, the use of one-dimensional SCNs potentially demolishes the spatialinformation of images, and may result in undesirable performance. This paperextends the original SCNs to two-dimensional version, termed 2DSCNs, for fastbuilding randomized learners with matrix-inputs. Some theoretical analyses onthe goodness of 2DSCNs against SCNs, including the complexity of the randomparameter space, and the superiority of generalization, are presented.Empirical results over one regression, four benchmark handwritten digitsclassification, and two human face recognition datasets demonstrate that theproposed 2DSCNs perform favourably and show good potential for image dataanalytics.

Two Dimensional Stochastic Configuration Networks for Image Data Analytics