Using Katz Centrality to Classify Multiple Pattern Transformations

Among the many machine learning methods developed for classification tasks, the network-based learning algorithms made great success. Usually, these methods consist of two stages: the construction of a network from the original vector-based data set and the learning in the constructed network. In this paper, a network concept, called vertex centrality, is used to perform pattern classification. A group of multiple invariant transformations of a same pattern is given and the network classifier must predict the pattern class the group belongs to. The prediction is based on the Katz centrality network measurement. Due to the ability of characterizing topological structure of input patterns, the method has been shown very competitive comparing to some state-of-the-art methods.