A Coupled Similarity Kernel for Pairwise Support Vector Machine

Support vector machine is a supervised learning model with associated learning algorithms that analyzes data and recognizes patterns. In various applications, the SVM shows its advantage of the classification performance, however, the original SVM was designed for the numerical data. For using the SVM on the nominal data, most previous research used a certain number to replace each nominal value or transformed the nominal value into the one hot vector. Both methods could not present the original nominal data's structure and the similarity between them, which leads to information loss from the data and reduce the classification performance. In this work, we design a novel coupled similarity metric between nominally attributed data. This metric is pairwise, we also propose an adapted SVM which can handle this. The experiment result shows the proposed method outperforms the traditional SVM and other popular classification methods on various public data sets.