Z
Zhenwen Ren
Researcher at Southwest University of Science and Technology
Publications - 69
Citations - 705
Zhenwen Ren is an academic researcher from Southwest University of Science and Technology. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 9, co-authored 36 publications receiving 239 citations. Previous affiliations of Zhenwen Ren include Nanjing University & Nanjing University of Science and Technology.
Papers
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Simultaneous Global and Local Graph Structure Preserving for Multiple Kernel Clustering
Zhenwen Ren,Quansen Sun +1 more
TL;DR: A novel MKL method, structure-preserving multiple kernel clustering (SPMKC), which proposes a new kernel affine weight strategy to learn an optimal consensus kernel from a predefined kernel pool, which can assign a suitable weight for each base kernel automatically.
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Robust low-rank kernel multi-view subspace clustering based on the Schatten p-norm and correntropy
TL;DR: A robust low-rank kernel multi-view subspace clustering approach that combines the non-convex Schatten p-norm ( 0 p ≤ 1 ) regularizer with the “kernel trick”, which can efficiently deal with problems that have non-linear structures in multi- view data via non- Convex methods.
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Learning Latent Low-Rank and Sparse Embedding for Robust Image Feature Extraction
TL;DR: A novel method to exploit the latent discriminative features of low rank embedding by utilizing an orthogonal matrix to hold the main energy of the original data and introducing an $\ell _{2,1}$ -norm term to encourage the features to be more compact, discrim inative and interpretable.
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Consensus Affinity Graph Learning for Multiple Kernel Clustering
TL;DR: This article proposes a new MKGC method to learn a consensus affinity graph directly via a thin autoweighted fusion model, in which a self-tuned Laplacian rank constraint and a top- $k$ neighbors sparse strategy are introduced to improve the quality of the consensus affinitygraph for accurate clustering purposes.
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Joint correntropy metric weighting and block diagonal regularizer for robust multiple kernel subspace clustering
TL;DR: The proposed Joint Robust Multiple Kernel Subspace Clustering (JMKSC) method for data clustering significantly outperforms several state-of-the-art single kernel and multiple kernel subspace clustering methods in terms of accuracy, NMI and purity.