A unified multi label classification framework with supervised low dimensional embedding
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.
Neurocomputing