Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k-means clustering of the autoencoder’s embedding. The diachronic setting, however, prevents the former to benefit from valuable information acquired by the latter. In this paper, we present an alternative where the autoencoder and the clustering are learned simultaneously. This is achieved by providing novel theoretical insight, where we show that the objective function of a certain class of Gaussian mixture models (GMM’s) can naturally be rephrased as the loss function of a one-hidden layer autoencoder thus inheriting the built-in clustering capabilities of the GMM. That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding. Experiments confirm the equivalence between the clustering module and Gaussian mixture models. Further evaluations affirm the empirical relevance of our deep architecture as it outperforms related baselines on several data sets.

深度嵌入聚类已成为使用深度神经网络对对象进行无监督分类的主要方法。最流行的方法的优化在深度自动编码器的训练和k- 表示自动编码器嵌入的聚类。然而，历时环境阻止了前者从后者获得的有价值的信息中受益。在本文中，我们提出了一种同时学习自动编码器和聚类的替代方法。这是通过提供新的理论见解来实现的，我们展示了某一类高斯混合模型 (GMM) 的目标函数可以自然地改写为单隐藏层自动编码器的损失函数，从而继承了内置的聚类功能GMM 的。这个简单的神经网络，称为聚类模块，可以集成到一个深度自动编码器中，从而产生一个能够联合学习聚类和嵌入的深度聚类模型。实验证实了聚类模块和高斯混合模型之间的等效性。进一步的评估证实了我们深层架构的经验相关性，因为它在几个数据集上的表现优于相关基线。