2020/08/16 by Junbing Li, Li, Junbing, Changqing Zhang +9
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Spam and Phishing Detection #Text and Document Classification Technologies #Web Data Mining and Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.2008.06883
arxiv created 2020/08/16 · openalex publication_date 2020/08/16 · arxiv updated 2020/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels. Furthermore, modeling the relationships between input and some (dull) classes further increases the difficulty of accurately predicting all possible labels. In this work, we propose to select a small subset of labels as landmarks which are easy to predict according to input (predictable) and can well recover the other possible labels (representative). Different from existing methods which separate the landmark selection and landmark prediction in the 2-step manner, the proposed algorithm, termed Selecting Predictable Landmarks for Multi-Label Learning (SPL-MLL), jointly conducts landmark selection, landmark prediction, and label recovery in a unified framework, to ensure both the representativeness and predictableness for selected landmarks. We employ the Alternating Direction Method (ADM) to solve our problem. Empirical studies on real-world datasets show that our method achieves superior classification performance over other state-of-the-art methods.