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A unified multi-label classification framework with supervised low-dimensional embedding
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DOI：10.1016/j.neucom.2015.07.087.png)
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Abstract
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It is an important issue for multi-label classification to discover and utilize data structures or label correlations during the learning process, which could greatly improve the learning performance. In this paper, a unified framework is proposed for multi-label classification by incorporating the supervised low-dimensional embedding into the predictive model. The supervised embedding exploits latent structures and correlations from samples and labels, finds informative shared characteristics in a low-dimensional subspace and obtains a high quality dimensionality reduction. In the framework, a low-dimensional feature mapping is constructed through a linear transformation guided by the label information; meanwhile, the weights of the multi-label classifier have already been set up. The framework leads to a trace optimization problem and can be solved by a generalized eigenvalue problem. The dual form of the framework is also proposed to deal with high-dimensional cases. Experiments on ten datasets show that the proposed unified framework achieves better or comparable performance in terms of multi-label classification measures and ranking measures and needs much less training time in most cases. Furthermore, the framework is robust to the size of the low-dimensional subspace. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Multi-label classification
Data structure
Label correlation
Supervised low-dimensional embedding
Dimensionality reduction
Generalized eigenvalue problem
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