Music Retagging Using Label Propagation and Robust Principal Component Analysis

TitleMusic Retagging Using Label Propagation and Robust Principal Component Analysis
Publication TypeConference Paper
Year of Publication2012
Conference Name21st International World Wide Web Conference (WWW 2012): 4th International Workshop on Advances in Music Information Research (AdMIRe 2012)
AuthorsYang, Y., Bogdanov D., Herrera P., & Sordo M.
Conference Start Date17/04/2012
Conference LocationLyon, France
AbstractThe 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.
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