Research on Multi-Degree-of-Freedom Neurons with Weighted Graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model — Multi-Degree-of-Freedom Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-offreedom neurons. It identifies the sample points set’s topological character in the feature space, which is different from the traditional “separation” method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.