用自编码器做无监督聚类，让深度学习更懂数据分组。
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
- 用自编码器学低维非线性特征，无需标签和领域知识
- 在低维空间聚类效果更好，缓解维度灾难问题
- 适合想入门深度聚类的新手，打基础用
自编码器能无监督地从数据中学习低维、非线性的表示，且不依赖特定数据类型或领域知识。其通用性和无领域依赖性使其成为研究与开发新型深度聚类算法的理想实验平台。聚类任务基于相似性分组数据，而自编码器学到的低维表示有助于缓解高维数据带来的挑战。将深度学习与聚类结合形成的深度聚类方法，可学习针对特定聚类任务优化的表示，从而获得高质量结果。本综述介绍基于自编码器的深度聚类基本算法，这些算法构成了众多现代方法的基础。
原文摘要 · Abstract (English)
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about the data type or other domain knowledge. The generality and domain agnosticism in combination with their simplicity make autoencoders a perfect sandbox for researching and developing novel (deep) clustering algorithms. Clustering methods group data based on similarity, a task that benefits from the lower-dimensional representation learned by an autoencoder, mitigating the curse of dimensionality. Specifically, the combination of deep learning with clustering, called Deep Clustering, enables to learn a representation tailored to specific clustering tasks, leading to high-quality results. This survey provides an introduction to fundamental autoencoder-based deep clustering algorithms that serve as building blocks for many modern approaches.
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