An end-to-end distance measuring for mixed data based on deep relevance learning

Abstract

Distance Measuring between two mixed data objects is the basis of many learning algorithms. The complex relevance between heterogeneous – various types/scales – attributes has a significant influence on the measured results. In this paper, we propose an End-to-End Distance Measuring method for mixed data based on deep relevance learning, called E ${}^2$ DM. Existing methods confuse the attributes space by mapping the discrete attribute values to new continuous values, or discretize continuous attributes values without considering the relevance. In contrast, E ${}^2$ DM directly manipulates on the original data with data conversion and relevance learning simultaneously to avoid information loss and attribute space confusion. E ${}^2$ DM firstly estimates internal relevance (i.e., relevance within the attribute) influenced distance by considering the categorical attribute value frequency and mapping numerical attribute values into multiple bins. Then it takes a wrapper approach to iteratively optimize relevance influenced distance and bin boundaries using a Frobenius-norm deviation as its objective function. Co-occurrence Mover’s Distance is proposed to explicitly explore relevance between attributes in each iteration. Finally, the distance for numerical attribute values is refined based on the original values and the fallen bin centers. Experimental results on a number of real-world datasets demonstrate that E ${}^2$ DM outperforms the state-of-the-art methods.