Label Propagation Using Amendable Clamping

Assigning several labels to digital data is becoming easier because we can perform it in a collaborative manner with Internet users. However, some suitable labels may be missed and may not be attached to the data leading to inaccuracies in classification. In this paper, we propose a novel graphbased multi-label classifier to support the multi-labeling task. The core process of our algorithm is to update label weights of labeled data from their top-k similar data in each label propagation step. We report that our algorithm is more stable for F-scores compared to the state-of-the-art ones even though the some correct labels are missed. ACM Classification