Abstract
Semi-supervised clustering algorithms have been proposed to identify data clusters that align with some side information provided by users. However, the identified clusters are still far from the true clusters perceived by users, mainly due to the sampling bias—traditional instance-level side information may cover a few, non-randomly sampled instances that mislead the algorithms to wrong clusters. To overcome this problem, a related work proposes to learn from the feature-level side information: perception vectors. However, the existing method assumes a linear correlation between the data features and perception features, which can not capture the nonlinearity correlation in some applications. In this paper, we propose two approaches Nonlinear Perception Embedded (NPE) and Neural Network (NN) to capture the nonlinear correlation between data and perception features and give better performance.