关键词: 距离学习, 混合距离, 距离函数, 边信息

Abstract: Learning distance function with side-information plays a key role in many data mining applications. Conventional metric learning approaches often use the distance function which is represented in form of Mahalanbobis distance which has some limitations. This paper proposes a new metric learning method with hybrid distance. In detail, the unknown distance metric is represented as the linear combination of several candidate distance metrics. A new distance function is achieved by learning weights with side-information. This paper applies the new distance function into clustering algorithm to verify the effectiveness. It also chooses the datasets from UCI machine learning repository to do experiments. The comparison with approaches for learning distance functions with side-information reveals the advantages of the proposed techniques.

Key words: distance metric learning, hybrid distance metric, distance function, side-information