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A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture - 2020

Research Area:  Machine Learning

Abstract:

Clustering is a fundamental problem in many data-driven application domains, and clustering performance highly depends on the quality of data representation. Hence, linear or non-linear feature transformations have been extensively used to learn a better data representation for clustering. In recent years, a lot of works focused on using deep neural networks to learn a clustering-friendly representation, resulting in a significant increase of clustering performance. In this paper, we give a systematic survey of clustering with deep learning in views of architecture. Specifically, we first introduce the preliminary knowledge for better understanding of this field. Then, a taxonomy of clustering with deep learning is proposed and some representative methods are introduced. Finally, we propose some interesting future opportunities of clustering with deep learning and give some conclusion remarks

Author(s) Name:  Erxue Min; Xifeng Guo; Qiang Liu; Gen Zhang; Jianjing Cui; Jun Long

Journal name:  IEEE Access

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/ACCESS.2018.2855437

Volume Information:  Volume: 6, Page(s): 39501 - 39514