An innovative multi-label learning based algorithm for city data computing

Investigating correlation between example features and example labels is essential to the solving of classification problems. However, identification and calculation of the correlation between features and labels can be rather difficult in case involving high-dimensional multi-label data. Both feature embedding and label embedding have been developed to tackle this challenge, and a shared subspace for both labels and features is usually learned by applying existing embedding methods to simultaneously reduce the dimension of features and labels. By contrast, this paper suggests learning separate subspaces for features and labels by maximizing the independence between the components in each subspace, as well as maximizing the correlation between these two subspaces. The learned independent label components indicate the fundamental combinations of labels in multi-label datasets, which thus helps to reveal the correlation between labels. Furthermore, the learned independent feature components lead to a compact representation of example features. The connections between the proposed algorithm and existing embedding methods are discussed in detail. Experimental results on real-world multi-label datasets demonstrate that it is necessary for us to explore independent components from multi-label data, and further prove the effectiveness of the proposed algorithm.