The latent space of autoencoders has been improved for clustering image data by jointly learning a t-distributed embedding with a clustering algorithm inspired by the neighborhood embedding concept proposed for data visualization. However, multivariate tabular data pose different challenges in representation learning than image data, where traditional machine learning is often superior to deep tabular data learning. In this paper, we address the challenges of learning tabular data in contrast to image data and present a novel Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacing t-distributions with multivariate Gaussian clusters. Unlike current methods, the proposed approach independently defines the Gaussian embedding and the target cluster distribution to accommodate any clustering algorithm in representation learning. A trained G-CEALS model extracts a quality embedding for unseen test data. Based on the embedding clustering accuracy, the average rank of the proposed G-CEALS method is 1.4 (0.7), which is superior to all eight baseline clustering and cluster embedding methods on seven tabular data sets. This paper shows one of the first algorithms to jointly learn embedding and clustering to improve multivariate tabular data representation in downstream clustering.

本文提出了一种基于高斯聚类嵌入的自编码器潜空间(G-CEALS)算法，该算法独立定义了高斯嵌入和目标聚类分布以适应任何表示学习中的聚类算法。结果显示：该方法的平均排名为1.4(0.7)，优于其他八种聚类方法。该算法是利用聚类和嵌入一起进行联合学习，以提高多元表格数据在聚类方面的表示。

G-CEALS: 自编码器潜在空间中的高斯聚类嵌入用于表格数据表示