Deep embedding clustering (DEC) attracts much attention due to its outperforming performance attributed to the end-to-end clustering. However, DEC cannot make use of small amount of a priori knowledge contained in data of increasing volume. To tackle this challenge, a semisupervised deep embedded clustering algorithm with adaptive labels is proposed to cluster those data in a semisupervised end-to-end manner on the basis of a little priori knowledge. Specifically, a deep semisupervised clustering network is designed based on the autoencoder paradigm and deep clustering, which well mine the clustering representation and clustering assignment by preventing the shift of labels in DEC. Then, to train parameters of the deep semisupervised clustering network, a back-propagation-based algorithm with adaptive labels is introduced based on the pretrain and fine-tune strategies. Finally, extensive experiments on representative datasets are conducted to evaluate the performance of the proposed method in terms of clustering accuracy and normalized mutual information. Results show the proposed method outperforms the state-of-the-art methods of DEC.

中文翻译：

---

具有自适应标签的半监督深度嵌入式群集

深度嵌入群集（DEC）由于其端到端群集的出色性能而备受关注。但是，DEC无法利用数量不断增加的数据中包含的少量先验知识。为了解决这一挑战，提出了一种具有自适应标签的半监督深度嵌入式聚类算法，该算法基于一点先验知识以半监督端到端的方式对这些数据进行聚类。具体来说，基于自动编码器范例和深度聚类设计了一个深半监督聚类网络，通过防止DEC中的标签偏移，很好地挖掘了聚类表示和聚类分配。然后，为了训练深度半监督聚类网络的参数，基于预训练和微调策略，引入了带有自适应标签的基于反向传播的算法。最后，在代表性数据集上进行了广泛的实验，以根据聚类准确性和归一化的互信息来评估所提出方法的性能。结果表明，所提出的方法优于DEC的最新方法。