Hits:

Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:NEUROCOMPUTING

Key Words:Multi-dimensional classification Problem transformation Distance metric learning Closed-form solution

Abstract:Multi-dimensional classification (MDC) refers to learning an association between individual inputs and their multiple dimensional output discrete variables, and is thus more general than multi-class classification (MCC) and multi-label classification (MLC). One of the core goals of MDC is to model output structure for improving classification performance. To this end, one effective strategy is to firstly make a transformation for output space and then learn in the transformed space. However, existing transformation approaches are all rooted in label power-set (LP) method and thus inherit its drawbacks (e. g., class imbalance and class overfitting). In this study, we first analyze the drawbacks of the LP, then propose a novel transformation method which can not only overcome these drawbacks but also construct a bridge from MDC to MLC. As a result, many off-the-shelf MLC methods can be adapted to our newlyformed problem. However, instead of adapting these methods, we propose a novel metric learning based method, which can yield a closed-form solution for the newly-formed problem. Interestingly, our metric learning based method can also naturally be applicable to MLC, thus itself can be of independent interest as well. Extensive experiments justify the effectiveness of our transformation approach and our metric learning based method. (C) 2017 Elsevier B.V. All rights reserved.

ISSN No.:0925-2312

Translation or Not:no

Date of Publication:2018-01-31

Co-author:Ma, Zhongchen

Correspondence Author:csc