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Semisupervised Multilabel Learning With Joint Dimensionality Reduction

文献类型：期刊论文

作者Yu, Tingzhao; Zhang, Wensheng; Tingzhao Yu
刊名IEEE SIGNAL PROCESSING LETTERS
出版日期2016-06-01
卷号23期号:6页码:795-799
关键词Alternating Method Dimensionality Reduction Dual Problem Multilabel Classification Semisupervised Learning
DOI10.1109/LSP.2016.2554361
文献子类Article
英文摘要Mutlilabel classification arises in various domains including computer vision and machine learning. Given a single instance, multilabel classification aims to learn a set of labels simultaneously. However, existing methods fail to address two key problems: 1) exploiting correlations among instances and 2) reducing computational complexity. In this letter, we propose a new semisupervised multilabel classification algorithm with joint dimensionality reduction. First, an elaborate matrix is designed for evaluating instance similarity; thus, it can take both labeled and unlabeled instances into consideration. Second, a linear dimensionality reduction matrix is added into the framework of multilabel classification. Besides, the dimensionality reduction matrix and the objective function can be optimized simultaneously. Finally, we design an efficient algorithm to solve the dual problem of the proposed model. Experiment results demonstrate that the proposed method is effective and promising.
WOS关键词TEXT CATEGORIZATION ; LABEL RANKING ; CLASSIFICATION
WOS研究方向Engineering
语种英语
WOS记录号WOS:000375305700005
源URL[http://ir.ia.ac.cn/handle/173211/11832]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Tingzhao Yu
作者单位Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Tingzhao,Zhang, Wensheng,Tingzhao Yu. Semisupervised Multilabel Learning With Joint Dimensionality Reduction[J]. IEEE SIGNAL PROCESSING LETTERS,2016,23(6):795-799.
APA Yu, Tingzhao,Zhang, Wensheng,&Tingzhao Yu.(2016).Semisupervised Multilabel Learning With Joint Dimensionality Reduction.IEEE SIGNAL PROCESSING LETTERS,23(6),795-799.
MLA Yu, Tingzhao,et al."Semisupervised Multilabel Learning With Joint Dimensionality Reduction".IEEE SIGNAL PROCESSING LETTERS 23.6(2016):795-799.

入库方式： OAI收割

来源：自动化研究所

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