Correlation-enhanced feature learning for multi-label classification

Label correlations take important roles in solving multi-label classification problems. However, many approaches extract label correlations from the corresponding label sets of data instances, which cannot provide satisfactory per-formance, because incompletely tagged label sets may exist in reality. To avoid such adverse effects, we directly extract label correlations from data instances and propose a multi-label approach called correlation-enhanced feature learning, named CeFL. First, a new revised label matrix is obtained by multiplying the incomplete label matrix with the label correlations, in which the label correlations are obtained from the data matrix rather than the incomplete label matrix. Then, the revised label information is used to expand the feature of the original data, and a neural network is used to learn the correlation-enhanced feature which contains relationships between data features, between labels and data features, and between labels. Compared with established approaches, ours is more intuitive and easier to understand, and label correlations obtained from the data can implicitly reflect the different importance of labels observed for identical data representation. Experiments on several databases demonstrate the effectiveness of our approach.