Multi-label dimensionality reduction based on semi-supervised discriminant analysis |
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Authors: | Hong Li Ping Li Yue-jian Guo and Min Wu |
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Affiliation: | School of Information Science and Engineering, Central South University, Changsha 410083, China |
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Abstract: | Multi-label data with high dimensionality often occurs, which will produce large time and energy overheads when directly used
in classification tasks. To solve this problem, a novel algorithm called multi-label dimensionality reduction via semi-supervised
discriminant analysis (MSDA) was proposed. It was expected to derive an objective discriminant function as smooth as possible
on the data manifold by multi-label learning and semi-supervised learning. By virtue of the latent imformation, which was
provided by the graph weighted matrix of sample attributes and the similarity correlation matrix of partial sample labels,
MSDA readily made the separability between different classes achieve maximization and estimated the intrinsic geometric structure
in the lower manifold space by employing unlabeled data. Extensive experimental results on several real multi-label datasets
show that after dimensionality reduction using MSDA, the average classification accuracy is about 9.71% higher than that of
other algorithms, and several evaluation metrices like Hamming-loss are also superior to those of other dimensionality reduction
methods. |
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Keywords: | |
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