Music retagging using label propagation and robust principal component analysis

Yang, Yi-Hsuan; Bogdanov, Dmitry; Herrera Boyer, Perfecto, 1964-; Sordo, Mohamed

doi:http://dx.doi.org/10.1145/2187980.2188217

Music retagging using label propagation and robust principal component analysis

Citation

Yang YH, Bogdanov D, Herrera P, Sordo M. Music retagging using label propagation and robust principal component analysis. In: WWW'12 Companion: Proceedings of the 21st International Conference on World Wide Web; 2012 Apr 16-20; Lyon, France. New York: Association for Computing Machinery; 2012. p. 869-76. DOI: 10.1145/2187980.2188217

Abstract

The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them to construct machine learning models. Nevertheless, such tags are usually noisy and sparse. In this paper, we present a preliminary study that aims at refining (retagging) social tags by exploiting the content similarity between tracks and the semantic redundancy of the track-tag matrix. The evaluated algorithms include a graph-based label propagation method that is often used in semi-supervised learning and a robust principal component analysis (PCA) algorithm that has led to state-of-the-art results in matrix completion. The results indicate that robust PCA with content similarity constraint is particularly effective; it improves the robustness of tagging against three types of synthetic errors and boosts the recall rate of music auto-tagging by 7% in a real-world setting.