Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning

• Introduces a novel Bayesian multilabel learning algorithm.
• Combines dimensionality reduction and classification for (semi-)supervised setup.
• Uses a deterministic variational approximation for efficient inference.
• Achieves good performance values in terms of hamming loss, average AUC, macro F1, and micro F1.
• Obtains very useful low-dimensional embeddings for exploratory data analysis.

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 first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F1F1, and micro F1F1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.