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An open-source tool for machine learning model
Xinyu Pu, Hangjun Che, Baicheng Pan
Published in Mathematics, 2023
This paper is about the robust graph-based multiview clustering. A novel multi-view clustering method referred to as RLGMC is proposed. In this method, we combine spectral embedding, low-rank tensor learning and noise constraints into a unified framework. By learning a robust tensor, the underlying structure implied in multiple views is effectively captured.
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Xinyu Pu, Hangjun Che, Member, IEEE, Baicheng Pan, Man-Fai Leung, and Shiping Wen
Published in IEEE Transactions on Computational Social Systems, 2023
In this paper, a robust multiview clustering method referred to as RWLTA is developed to handle the mixed noise embedding in the real-world datasets. The weighted low-rank tensor approximation and unique mixed noise handling norms are utilized to guarantee performance and robustness.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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