标题：
Ordinal factorization machine with hierarchical sparsity
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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：
FRONTIERS OF COMPUTER SCIENCE
关键字：
ordinal regression factorization machine hierarchical sparsity interaction modelling
摘要：
Ordinal regression (OR) or classification is a machine learning paradigm for ordinal labels. To date, there have been a variety of methods proposed including kernel based and neural network based methods with significant performance. However, existing OR methods rarely consider latent structures of given data, particularly the interaction among covariates, thus losing interpretability to some extent. To compensate this, in this paper, we present a new OR method: ordinal factorization machine with hierarchical sparsity (OFMHS), which combines factorization machine and hierarchical sparsity together to explore the hierarchical structure behind the input variables. For the sake of optimization, we formulate OFMHS as a convex optimization problem and solve it by adopting the efficient alternating directions method of multipliers (ADMM) algorithm. Experimental results on synthetic and real datasets demonstrate the superiority of our method in both performance and significant variable selection.
ISSN号：
2095-2228
是否译文：
否
发表时间：
2020-02-01
合写作者：
Guo, Shaocheng,Tian, Qing
通讯作者：
陈松灿
发表时间：
2020-02-01
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