In this paper we propose a Deep Autoencoder MIxture Clustering (DAMIC)
algorithm based on a mixture of deep autoencoders where each cluster is
represented by an autoencoder. A clustering network transforms the data into
another space and then selects one of the clusters. Next, the autoencoder
associated with this cluster is used to reconstruct the data-point. The
clustering algorithm jointly learns the nonlinear data representation and the
set of autoencoders. The optimal clustering is found by minimizing the
reconstruction loss of the mixture of autoencoder network. Unlike other deep
clustering algorithms, no regularization term is needed to avoid data
collapsing to a single point. Our experimental evaluations on image and text
corpora show significant improvement over state-of-the-art methods.

这篇论文提出了一种基于深度自编码器混合聚类（DAMIC）算法，该算法通过聚类网络将数据转化到另一个空间，并且提出了一种集合自编码器来表示每一个聚类，同时聚类算法共同学习非线性数据表述和集合自编码器，通过最小化混合自编码器网络的重构损失，找到最优的聚类，实验结果显示该方法在图像和文本上均优于现有技术。