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The latent space of autoencoders has been improved for clustering image databy jointly learning a t-distributed embedding with a clustering algorithminspired by the neighborhood embedding concept proposed for data visualization.However, multivariate tabular data pose different challenges in representationlearning than image data, where traditional machine learning is often superiorto deep tabular data learning. In this paper, we address the challenges oflearning tabular data in contrast to image data and present a novel GaussianCluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacingt-distributions with multivariate Gaussian clusters. Unlike current methods,the proposed approach independently defines the Gaussian embedding and thetarget cluster distribution to accommodate any clustering algorithm inrepresentation learning. A trained G-CEALS model extracts a quality embeddingfor unseen test data. Based on the embedding clustering accuracy, the averagerank of the proposed G-CEALS method is 1.4 (0.7), which is superior to alleight baseline clustering and cluster embedding methods on seven tabular datasets. This paper shows one of the first algorithms to jointly learn embeddingand clustering to improve multivariate tabular data representation indownstream clustering.

G-CEALS: Gaussian Cluster Embedding in Autoencoder Latent Space for Tabular Data Representation