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Yuanpeng Zhang
Researcher at Nantong University
Publications - 12
Citations - 239
Yuanpeng Zhang is an academic researcher from Nantong University. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 6, co-authored 9 publications receiving 141 citations. Previous affiliations of Yuanpeng Zhang include Jiangnan University & Hong Kong Polytechnic University.
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Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules
TL;DR: It is shown that a linguistic rule with the outputs from the previous layers as its inputs is equivalent to a fuzzy rule with a nonlinear consequent or a linear consequent with a certainty factor, and that HID-TSK-FC is mathematically equivalents to a novel TSK fuzzy classifier with shared interpretable linguistic fuzzy rules.
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Fast Exemplar-Based Clustering by Gravity Enrichment Between Data Objects
TL;DR: Based on a new look at the Bayesian framework of data clustering, two new concepts are introduced and they correspond to a Bayesian information transmission system and its transmission learning and an exemplar-based transmission learning machine for clustering is developed.
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A Multiview and Multiexemplar Fuzzy Clustering Approach: Theoretical Analysis and Experimental Studies
TL;DR: This study demonstrates that multiview & multiexemplar fuzzy clustering has a theoretical guarantee of enhanced clustering performance, and develops a novel multi-view fuzziness clustering approach, M2FC, which outperforms the existing state-of-the-art multIView approaches in most cases.
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Fast Reduced Set-Based Exemplar Finding and Cluster Assignment
TL;DR: A new fast exemplar-based clustering approach is proposed for a dataset with an arbitrary shape and number of clusters and theoretically analyze the proposed FEF from the perspective of the generalization performance of clustering and demonstrates the power of the proposed approach on several benchmarking datasets.
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Clustering by transmission learning from data density to label manifold with statistical diffusion
TL;DR: As the first attempt to explain the clustering behavior in a lifelike way, LMTLMC is well justified by revealing the natural parallel between its gradient-based optimization process and the statistical diffusion in statistical physics through the modified Fick’s diffusion law for clustering.