Safe incomplete label distribution learning. (May 2022)

• Record Type: Journal Article
• Authors: Zhang, Jing; Tao, Hong; Luo, Tingjin; Hou, Chenping
• Is Part Of: Pattern recognition. Volume 125(2022)
• Publication Date: 2022-05
• Subjects: Label distribution learning -- Safeness -- Incomplete supervised learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108518
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We have proposed a novel method approach, i.e., SILDL, for safe label distribution learning. We have used the squared loss in objective function which could be formulated as a simple convex quadratic program and could be easily solved. We have defined an equivalent formulation to find out the best result from multiple baseline methods, and we have integrated multiple incomplete supervised learners to maximize the worst-case performance gain against the best baseline method. We have proved that the SILDL approach is provably safe with mild conditions. Abstract: Label Distribution Learning (LDL) is a popular scenario for solving label ambiguity problems by learning the relative importance of each label to a particular instance. Nevertheless, the label is often incomplete due to the difficulty in annotating label distribution. In this mixing label case with complete and incomplete labels, it is often expected that the learning method can achieve better performance than the baseline method merely utilizing complete labeled data. However, the usage of incomplete labeled data may degrade the performance in real applications. Therefore, it is vital to design a safe incomplete LDL method, which will not deteriorate the performance when exploiting incomplete labeled data. To tackle this important but rarely studied problem, we propose a Safe Incomplete LDL method (SILDL), which learns a classifier that can prevent incomplete labeled instances from worsening the performance. Concretely, we learn predictions from multiple incomplete supervised learners and design an efficient solving algorithm by formulating it as a convex quadratic program. Theoretically, we prove that SILDL can obtain the maximal performance gain against the best one of the multiple baseline methods with mild conditions. Extensive experimental results validate the safeness of the proposed approach and show improvements in performance.