2022/10/15 by Qimeng Guo, Guo, Qimeng, Zhuoran Zheng +5 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music and Audio Processing #Text and Document Classification Technologies #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2210.08184
openalex publication_date 2022/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Label distribution learning can characterize the polysemy of an instance through label distributions. However, some noise and uncertainty may be introduced into the label space when processing label distribution data due to artificial or environmental factors. To alleviate this problem, we propose a Label Correlation Grid (LCG) to model the uncertainty of label relationships. Specifically, we compute a covariance matrix for the label space in the training set to represent the relationships between labels, then model the information distribution (Gaussian distribution function) for each element in the covariance matrix to obtain an LCG. Finally, our network learns the LCG to accurately estimate the label distribution for each instance. In addition, we propose a label distribution projection algorithm as a regularization term in the model training process. Extensive experiments verify the effectiveness of our method on several real benchmarks.