Improvement on Higher-Order Neural Networks for Invariant Object Recognition

摘要

The higher order neural network(HONN) was proved to be able to realize invariant object recognition. By taking the relationship between input units into account, HONN's are superior to other neural models in invariant pattern recognition. However, there are two main problems preventing HONN's from practical applications. One is the combinatorial increase of weight number, that is, as input size increases, the number of weights in a HONN increases exponentially. The other problem is sensitivity to distortion and noise. In this paper, we described a method, in which by modifying the constraints imposed on the weights in HONN's, the performance of a HONN with respect to distortion can be improved considerably.