TY - JOUR AU - Sun, Lijuan AU - Feng, Songhe AU - Wang, Tao AU - Lang, Congyan AU - Jin, Yi PY - 2019/07/17 Y2 - 2024/03/28 TI - Partial Multi-Label Learning by Low-Rank and Sparse Decomposition JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v33i01.33015016 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4433 SP - 5016-5023 AB - <p>Multi-Label Learning (MLL) aims to learn from the training data where each example is represented by a single instance while associated with a set of candidate labels. Most existing MLL methods are typically designed to handle the problem of missing labels. However, in many real-world scenarios, the labeling information for multi-label data is always redundant , which can not be solved by classical MLL methods, thus a novel <em>Partial Multi-label Learning</em> (PML) framework is proposed to cope with such problem, i.e. removing the the noisy labels from the multi-label sets. In this paper, in order to further improve the denoising capability of PML framework, we utilize the low-rank and sparse decomposition scheme and propose a novel <em>Partial Multi-label Learning by Low-Rank and Sparse decomposition</em> (PML-LRS) approach. Specifically, we first reformulate the observed label set into a label matrix, and then decompose it into a groundtruth label matrix and an irrelevant label matrix, where the former is constrained to be low rank and the latter is assumed to be sparse. Next, we utilize the feature mapping matrix to explore the label correlations and meanwhile constrain the feature mapping matrix to be low rank to prevent the proposed method from being overfitting. Finally, we obtain the ground-truth labels via minimizing the label loss, where the Augmented Lagrange Multiplier (ALM) algorithm is incorporated to solve the optimization problem. Enormous experimental results demonstrate that PML-LRS can achieve superior or competitive performance against other state-of-the-art methods.</p> ER -