Robust Pattern Recognition by Interpolating Vectors

This paper proposes a powerful algorithm for pattern recognition, which uses interpolating vectors. Labeled reference vectors in a multi-dimensional feature space are first produced by a kind of competitive learning. We then assume that interpolating vectors are densely placed along line segments connecting all pairs of reference vectors of the same label. From these interpolating vectors, we choose the one that has the largest similarity to the test vector. Its label shows the result of pattern recognition. In practice, we can get the same result with a simpler process. We applied this method to the neocognitron for handwritten digit recognition. When the network had been trained with 5000 digits, the use of interpolating vectors reduced the error rate from 1.52% to 1.02% for a blind test set of 5000 digits. The use of interpolating vectors is not limited to the neocognitron but can be applied to various systems for pattern recognition.