Image clustering is one of the most important computer vision applications, which has been extensively studied in literature. However, current clustering methods mostly suffer from lack of efficiency and scalability when dealing with large-scale and high-dimensional data. In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convolutional autoencoder. We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of clusters. An alternating strategy is then derived to optimize the objective by updating parameters and estimating cluster assignments. Furthermore, we employ the reconstruction loss functions in our autoencoder, as a data-dependent regularization term, to prevent the deep embedding function from overfitting. In order to benefit from end-to-end optimization and eliminate the necessity for layer-wise pretraining, we introduce a joint learning framework to minimize the unified clustering and reconstruction loss functions together and train all network layers simultaneously. Experimental results indicate the superiority and speed of DEPICT and our joint learning approach in real-world clustering tasks, in which no labeled data is available for hyper-parameter tuning.

该研究提出了一种名为DEPICT的新型深度嵌入式正则化聚类模型，该模型使用多层卷积自编码器和多项式 logistic 回归函数，通过相对熵最小化方法实现效率和精确性高效的数据映射和聚类分配，可以在无标签数据进行超参数调整的真实世界聚类任务中发挥其优越性和更快的运行时间。

基于卷积自编码嵌入和相对熵最小化的深度聚类