2023/03/21 by Zhiqiang Kou, Yuheng Jia, Kou, Zhiqiang +7
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Music and Audio Processing #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.2303.11698
openalex publication_date 2023/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Label distribution (LD) uses the description degree to describe instances, which provides more fine-grained supervision information when learning with label ambiguity. Nevertheless, LD is unavailable in many real-world applications. To obtain LD, label enhancement (LE) has emerged to recover LD from logical label. Existing LE approach have the following problems: (i) They use logical label to train mappings to LD, but the supervision information is too loose, which can lead to inaccurate model prediction; (ii) They ignore feature redundancy and use the collected features directly. To solve (i), we use the topology of the feature space to generate more accurate label-confidence. To solve (ii), we proposed a novel supervised LE dimensionality reduction approach, which projects the original data into a lower dimensional feature space. Combining the above two, we obtain the augmented data for LE. Further, we proposed a novel nonlinear LE model based on the label-confidence and reduced features. Extensive experiments on 12 real-world datasets are conducted and the results show that our method consistently outperforms the other five comparing approaches.