Adaptive kernel learning based on centered alignment for hierarchical classification

Hierarchical classification, decomposing the multi-class classification problem into binary ones hierarchically, is efficient when the class quantity getting large. Nowadays, the variety of features to describe data becomes huge and meanwhile the form of these features is diverse, which both make the task of feature fusion crucial for classification. In this paper, an adaptive kernel learning method, which resorts to kernel combination for feature fusion, is proposed and incorporated into the hierarchical classification framework for the multi-class and multi-feature classification scenario. By the centered kernel alignment, the tasks of category partition and kernel combination are unified into a coherent optimization problem, and an iterative algorithm is designed to solve it. Experimental results on two datasets show that our method is not only efficient but also accurate compared with other baseline methods.