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Music retagging using label propagation and robust principal component analysis abstract.
| Content Provider | CiteSeerX |
|---|---|
| Author | Yang, Yi-Hsuan |
| 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 particularlyeffective; itimprovestherobustnessoftagging against three types of synthetic errors and boosts the recall rate of music auto-tagging by 7 % in a real-world setting. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Robust Principal Component Analysis Abstract Label Propagation Recall Rate Robust Pca Machine Learning Model Preliminary Study Semantic Redundancy Social Tag Synthetic Error Semi-supervised Learning Content Similarity Constraint Content Similarity Matrix Completion Evaluated Algorithm Robust Principal Component Analysis Track-tag Matrix Computational Model Music Tag Music Auto-tagging Graph-based Label Propagation Method Real-world Setting State-of-the-art Result New Opportunity |
| Content Type | Text |
