Reduced shift invariant second order neural networks using principal component analysis and pixel combinations
Bongkyu Lee, Dong‐Kyu Kim, Yookun Cho, H.H. Lee, Hee-Yeung Hwang · 2002
When two dimensional images are used as input to a neural network, the noise from the input device and small deformations in the end parts that occur in the processes of separating each pattern and size normalization lead to images shifted from the original learned image being input to the neural network which is a major cause of misrecognition. The inputs of the second order neural network (SONN) described consist of geometrically related nonlinear combinations of two pixels, and can be used for shift invariant pattern recognition. But the number of SONN input nodes increases in proportion to N/sup 2/, where N is the dimension of the input patterns, even if we only consider the shift invariance. Such large number of input nodes lead to slower learning aid recognition. In this paper, we propose a method for reducing the number of shift invariant SONN input nodes using combinations of input pattern pixels and the principle component analysis. Using the proposed method we are able to implement a shift invariant second order neural network with 2/5*N nodes. Due to the reduced number of input nodes, a 50% reduction in the learning and recognition time was obtained.>