标题：
A convex formulation for multiple ordinal output classification
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所属单位：
计算机科学与技术学院/人工智能学院/软件学院
发表刊物：
Pattern Recogn.
摘要：
Multiple ordinal output classification (MOOC) which specifically refers to learning an association between individual inputs (e.g. face images) and a set of discrete ordinal response/output variables (e.g. facial action units), is a special case of multi-output classification and also a relatively-understudied topic in machine learning. It is very challenging in how to jointly model the relationship among multiple output variables and their discrete ordinal values. In this paper, we propose an effective formulation to address the above challenging problems. Under this formulation, the objective function is convex and thus leads to a convex multiple ordinal output classification (ConMOOC). Specifically, we use a regularization formulation to model the relationships among multiple output variables and an effective threshold-based loss function to fit their ordinal values. To enhance ability of the model, we also apply the kernel trick to provide a nonlinear extension. For efficiency, we use an alternating iteration method to learn the optimal model parameters for each variable as well as the relationships between different variables. Experiments conducted on synthetic and real datasets demonstrate that ConMOOC not only achieves effective classification performance but also reveals the structures among output variables. To the best of our knowledge, MOOC as a general machine learning task is the first time to be studied. © 2018 Elsevier Ltd
ISSN号：
0031-3203
是否译文：
否
发表时间：
2019-02-01
合写作者：
Ma, Zhongchen
通讯作者：
陈松灿
发表时间：
2019-02-01
学位：工学博士学位
职称：教授
所在单位：计算机科学与技术学院/人工智能学院/软件学院