A semi-supervised label distribution learning model with label correlations and data manifold exploration

Label distribution learning is a novel machine learning paradigm to deal with label ambiguity, which is the generalization of the traditional single-label learning and multi-label learning paradigms. Though label distribution learning has attracted a lot of attentions recently, data sets with label distributions rather than logical labels have always been scarce and most of the existing models put emphasis mainly on the supervised learning, neglecting the utilization of unlabeled samples. In this paper, we propose a semi-supervised Label Distribution Learning model with label Correlations and data Manifold exploration (sLDLCM). On one hand, sLDLCM is a semi-supervised extension of existing label distribution learning models, which can effectively make use of both labeled and unlabeled data for better capturing the underlying data properties; on the other hand, sLDLCM jointly estimates the label distributions of unlabeled samples and the other model variables. Besides, both label correlations and local data manifold are explored in sLDLCM. Extensive experiments are conducted on six real-world data sets including two facial expression, one movie rating, two bioinformatics and one visual sentiment analysis data sets. Comparative studies demonstrate that the proposed sLDLCM model achieves better performance the state-of-the-arts in terms of five evaluation metrics.

Automated summary

In this paper, a semi-supervised Label Distribution Learning model with label Correlations and data Manifold exploration (sLDLCM) is proposed. It effectively utilizes both labeled and unlabeled data to capture the underlying data properties and jointly estimates the label distributions of unlabeled samples and other model variables. Experimental results on multiple tasks demonstrate that the proposed sLDLCM model outperforms the state-of-the-arts.