A label distribution manifold learning algorithm. (March 2023) - Tan, Chao
Highlights: Propose label distribution manifold learning for multilabel data. Accurate reduce-dimension feature extraction for data, and kernel regression for estimating label distributions associated with extracted features. Enhance maximum entropy estimation of label distributions for multilabel data with high accuracy and low complexity. Abstract: In this paper, we propose a novel label distribution manifold learning (LDML) method for solving the multilabel distribution learning problem. First, using manifold learning, we extract the accurate and reduced-dimension features of the training data. Second, we estimate the unknown label distributions associated with the extracted reduced-dimension features based on multi-output kernel regression. Third, we use the extracted reduced-dimension features and their associated estimated label distributions to form an enhanced maximum entropy model, which enables us to accurately and efficiently estimate the unknown true label distributions for the training data. We refer to this algorithm as the LDML. We also propose to apply the tangent space alignment regression in the second stage, and the resulting algorithm is called the LDML-R. The LDML-R has better label distribution learning performance than the LDML but imposes higher complexity than the latter. We evaluate the proposed LDML and LDML-R algorithms on 15 real-world data sets with ground-truth label distributions, and the experimental results obtained show that our method has advantages in terms of learning accuracy compared to the latest multi-label distribution learning approaches. We also use another 10 real-world multi-class data sets, which do not have the ground-truth label distributions, to demonstrate the superior multilabel classification performance of our LDML-R algorithm over the existing state-of-the-art multi-label classification algorithms.