Bayesian Supervised Multilabel Learning with Coupled Embedding and Classification

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we introduce a novel Bayesian supervised multilabel learning method that combines linear dimensionality reduction with linear binary classification. We present a deterministic variational approximation approach to learn the proposed probabilistic model for multilabel classification. We perform experiments on four benchmark multilabel learning data sets by comparing our method with four baseline linear dimensionality reduction algorithms. Experiments show that the proposed approach achieves good performance values in terms of hamming loss, macro F1, and micro F1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis.