2016
Learning User Perceived Clusters with Feature-Level Supervision
NeurIPS 2016poster
Semi-supervised clustering algorithms have been proposed to identify data clusters that align with user perceived ones via the aid of side information such as seeds or pairwise constrains. However, traditional side information is mostly at the instance level and subject to the sampling bias, where n…