基于类间损失和多视图融合的深度嵌入聚类

针对深度嵌入聚类方法仅考虑类内关系及多视图聚类存在特征表示不足等问题，提出一种基于类间损失和多视图特征融合的深度嵌入聚类方法，该方法在深度嵌入聚类的损失函数中引进一个新的正则项提高类判别性.先通过自动编码器提取多视图数据的特征表示，对不同视图的特征表示进行融合得到公共表示，基于此得到数据的软分配分布和辅助目标分布；再对公共表示和聚类分配进行联合优化得到最终的聚类结果.在多视图数据集上的实验结果表明，该方法能有效提高聚类性能.

Deep Embedding Clustering Based on Inter-Class Loss and Multi-view Fusion

Aiming at the problem that the deep embedding clustering method only considerd the intra-class relationship and multi-view clustering had insufficient feature representation, we proposed a deep embedding clustering method based on inter-class loss and multi-view fusion. The method introduced a new regularization term into the loss function of deep embedding clustering to improve the class discrimination. Firstly, the feature representations of multi-view data were extracted by the auto-encoder, and the feature representations of different views were fused to obtain the public representation. Based on this, the soft allocation distribution and auxiliary target distribution of data were obtained. Secondly, the final clustering result was obtained by jointly optimizing the public representation and cluster allocation. The experimental results on multi-view datasets show that this method can effectively improve the clustering performance.