  Stochastic configuration networks (SCNs) as a class of randomized learner
model have been successfully employed in data analytics due to its universal
approximation capability and fast modelling property. The technical essence
lies in stochastically configuring hidden nodes (or basis functions) based on a
supervisory mechanism rather than data-independent randomization as usually
adopted for building randomized neural networks. Given image data modelling
tasks, the use of one-dimensional SCNs potentially demolishes the spatial
information of images, and may result in undesirable performance. This paper
extends the original SCNs to two-dimensional version, termed 2DSCNs, for fast
building randomized learners with matrix-inputs. Some theoretical analyses on
the goodness of 2DSCNs against SCNs, including the complexity of the random
parameter space, and the superiority of generalization, are presented.
Empirical results over one regression, four benchmark handwritten digits
classification, and two human face recognition datasets demonstrate that the
proposed 2DSCNs perform favourably and show good potential for image data
analytics.
