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In this paper, we present a Mirroring Neural Network architecture to performnon-linear dimensionality reduction and Object Recognition using a reducedlowdimensional characteristic vector. In addition to dimensionality reduction,the network also reconstructs (mirrors) the original high-dimensional inputvector from the reduced low-dimensional data. The Mirroring Neural Networkarchitecture has more number of processing elements (adalines) in the outerlayers and the least number of elements in the central layer to form aconverging-diverging shape in its configuration. Since this network is able toreconstruct the original image from the output of the innermost layer (whichcontains all the information about the input pattern), these outputs can beused as object signature to classify patterns. The network is trained tominimize the discrepancy between actual output and the input by backpropagating the mean squared error from the output layer to the input layer.After successfully training the network, it can reduce the dimension of inputvectors and mirror the patterns fed to it. The Mirroring Neural Networkarchitecture gave very good results on various test patterns.

Dimensionality Reduction and Reconstruction using Mirroring Neural Networks and Object Recognition based on Reduced Dimension Characteristic Vector