Meta-learning representations for clustering with infinite Gaussian mixture models

• Published in: Neurocomputing (Amsterdam) Vol. 549; p. 126423
• Main Author: Iwata, Tomoharu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 07.09.2023
• Subjects: Clustering; Deep learning; Gaussian mixture; Meta-learning; Deep learning; Gaussian mixture; Clustering; Meta-learning
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2023.126423

Appropriate representations are critical for a better clustering performance. Although many neural network-based clustering methods have been proposed, they do not directly train neural networks to improve the clustering performance. We propose a method that can meta-learn knowledge for clustering from various labeled data and uses the knowledge for clustering unseen unlabeled data. The proposed method trains neural networks to obtain representations such that the clustering performance improves when the representations are clustered by variational Bayesian (VB) inference with an infinite Gaussian mixture model. For the objective function, we propose a continuous approximation of the adjusted Rand index (ARI) by which we can evaluate the clustering performance from soft clustering assignments. Since the approximated ARI and the VB inference procedure are differentiable, we can backpropagate the objective function through the VB inference procedure to train the neural networks. With experiments using text and image datasets, we demonstrate that our proposed method has a higher adjusted Rand index than the existing methods.