Abstract
This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep Bayesian clustering framework is proposed. In particular, a heterogeneous feature metric is first constructed to measure the similarity between different types of features. Then, a feature metric-restricted hierarchical sample generation process is established, in which sample with heterogeneous features is clustered by generating it from a similarity constraint hidden space. When estimating the model parameters and posterior probability, the corresponding variational inference algorithm is derived and implemented. To verify our model capability, we demonstrate our model on the synthetic dataset and show the superiority of the proposed method on some real datasets. Our source code is released on the website: Github.com/yexlwh/Heterogeneousclustering.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62006131, 62071260), the National Natural Science Foundation of Zhejiang Province (LQ21F020009, LQ18F020001).
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Xulun Ye received the MSc and PhD degrees from Ningbo University, China in 2016 and 2019, respectively, where he is currently a lecturer. His research interests include Bayesian learning, deep learning, nonparametric clustering and convex analysis.
Jieyu Zhao received the BS and MSc degrees from Zhejiang University, China and the PhD degree from Royal Holloway University of London, UK in 1985, 1988 and 1995 respectively. He is currently a full professor at Ningbo University, China. His research interests include deep learning, and computer vision.
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Ye, X., Zhao, J. Heterogeneous clustering via adversarial deep Bayesian generative model. Front. Comput. Sci. 17, 173322 (2023). https://doi.org/10.1007/s11704-022-1376-2
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DOI: https://doi.org/10.1007/s11704-022-1376-2