Abstract: In multi-label learning, the subjectivity and instability of manual labeling often result in the absence of partial class labels and an incomplete label space. Missing labels are likely to be misleading in the training of supervised learning algorithms. The use of label correlation can alleviate the negative effect of missing labels on the algorithm classification performance to a certain extent. However, missing labels can lead to inaccurate estimations of label correlations. To address this problem, a method called multi-label learning with incomplete labels using an augmented label correlation matrix (ML-ALC) was proposed. First, a low-dimensional manifold of the data is constructed using Laplace mapping, and the original label correlation matrix is calculated using the label vectors. Subsequently, a correction matrix is constructed to augment the original label correlation matrix, and the original feature space and label space are mapped to the low-dimensional manifold through a regression coefficient matrix and augmented label correlation matrix, respectively. Finally, the optimized regression coefficient matrix and augmented label correlation matrix are obtained through iterative learning and applied to multi-label classification. The experimental results demonstrate that the proposed algorithm performs better than other state-of-the-art methods in multi-label learning with incomplete labels.

Key words: multi-label learning with incomplete labels, missing labels, label correlation, low-dimensional manifold