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Semisupervised Multilabel Learning With Joint Dimensionality Reduction
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DOI：10.1109/LSP.2016.2554361.png)
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摘要
En 中文
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.
Keyword:
Alternating method
dimensionality reduction
dual problem
multilabel classification
semisupervised learning
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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