Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/629095
Title: Semisupervised Multilabel Learning With Joint Dimensionality Reduction
Authors: Tingzhao Yu;Wensheng Zhang
subject: Semi-supervised learning|Alternating method|Dimensionality reduction|Dual problem|Multi-label classification
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/158361
http://localhost/handle/Hannan/629095
ISSN: 1070-9908
1558-2361
volume: 23
issue: 6
Appears in Collections:2016

Files in This Item:
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Title: Semisupervised Multilabel Learning With Joint Dimensionality Reduction
Authors: Tingzhao Yu;Wensheng Zhang
subject: Semi-supervised learning|Alternating method|Dimensionality reduction|Dual problem|Multi-label classification
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/158361
http://localhost/handle/Hannan/629095
ISSN: 1070-9908
1558-2361
volume: 23
issue: 6
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7452598.pdf501.41 kBAdobe PDFThumbnail
Preview File
Title: Semisupervised Multilabel Learning With Joint Dimensionality Reduction
Authors: Tingzhao Yu;Wensheng Zhang
subject: Semi-supervised learning|Alternating method|Dimensionality reduction|Dual problem|Multi-label classification
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/158361
http://localhost/handle/Hannan/629095
ISSN: 1070-9908
1558-2361
volume: 23
issue: 6
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7452598.pdf501.41 kBAdobe PDFThumbnail
Preview File