OPM2L: An optimal instance partition-based multi-metric learning method for heterogeneous dataset classification

Abstract

Multi-metric learning -a method to learn multiple local metrics to reveal the feature's correlations of samples from different local regions-has become an essential tool to measure the similarities between instances from heterogeneous datasets. However, most existing cluster-based MML methods first partition the training data with a predefined metric and then learn multiple metrics via the local instances, leading to these two independent procedures fail to cooperate with each other. In this paper, we propose an Optimal instance Partition-based Multi-Metric Learning (OPM2L) method for heterogeneous dataset classification by unifying the instance partition and multiple local metrics learning into a single objective. In particular, multiple anchor centers together with a global metric are employed to assist the instance partition process. During the training, the shared information contained in local metrics is aggregated into the global metric by a dedicated regularizer, which improves the instance partition process and offers the subsequent multiple local metrics learning with more informative instances. Moreover, an efficient alternating direction technology is employed to seek a feasible solution to the proposed method. We further confirmed that the sub-problems can be settled with closed-form solutions, while the superiority of the proposed method is also proved by experimental results on extensive datasets.