2022/01/01 by Zhongchen Ma, Songcan Chen · 7 citations
Computer Science · Engineering · #Artificial intelligence #Computer science #Engineering #Image Retrieval and Classification Techniques #Machine Learning and Data Classification #Natural language processing #Pattern recognition (psychology) #Similarity (geometry) #Task (project management) #Text and Document Classification Technologies #cs.LG
paper · pdf · doi:10.1109/tkde.2022.3151979
published in IEEE Transactions on Knowledge and Data Engineering, 1 (IEEE Computer Society) · Accepted by TKDE2022
openalex publication_date 2022/01/01 · arxiv created 2022/03/05 · arxiv updated 2022/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification task. Specifically, we unite similarity-based learning and generalized linear models to achieve the best of both worlds. This allows us to capture interdependencies between classes and prevent from impairing performance of noisy classes. Each learned parameter of the model can reveal the contribution of one class to another, providing interpretability to some extent. Experiment results show the effectiveness of the proposed approach on multi-class and multi-label datasets