Partial Multi-label Learning Based On Near-Far Neighborhood Label Enhancement And Nonlinear Guidance

Yu Chen, Yanan Wu, Na Han, Xiaozhao Fang, Bingzhi Chen, Jie Wen

Abstract: Partial multi-label learning (PML) deals with the problem of accurately predicting the correct multi-label class for each instance in multi-label data containing noise. Compared with traditional multi-label learning, partial multi-label learning requires learning and completing multi-label classification tasks in an imperfect environment. The existing PML methods have the following problems: (1) the correlation between samples and labels is not fully utilized; (2) the nonlinear nature of the model is not taken into account. To solve these problems, we propose a new method of PML based on label enhancement of near and far neighbor information and nonlinear guidance (PML-LENFN). Specifically, the original binary label information is reconstructed by using the information of sample near neighbors and far neighbors to eliminate the influence of noise. Then we construct a linear multi-label classifier that can explore label correlation. In order to learn the nonlinear relationship between features and labels, we use nonlinear mapping to constrain this classifier, so as to obtain the prediction results that are more consistent with the realistic label distribution.

Primary Subject Area: [Systems] Data Systems Management and Indexing

Secondary Subject Area: [Engagement] Multimedia Search and Recommendation, [Content] Multimodal Fusion

Relevance To Conference: This work contributes to multimedia/multimodal processing by improving the accuracy of multi-label classification in noisy and imperfect environments. By utilizing the information of sample near neighbors and far neighbors to enhance label information, the proposed method can effectively eliminate noise and improve the correlation between samples and labels. Additionally, the incorporation of nonlinear guidance allows the model to capture the nonlinear relationships between features and labels, leading to more accurate and realistic prediction results. This can be particularly beneficial in multimedia/multimodal processing applications where data may be complex and noisy, such as image or video classification tasks. Ultimately, the method can enhance the performance of multi-label classification in multimedia/multimodal processing, leading to more accurate and reliable results.

Submission Number: 3314