M
Ming Yin
Researcher at Guangdong University of Technology
Publications - 69
Citations - 1532
Ming Yin is an academic researcher from Guangdong University of Technology. The author has contributed to research in topics: Cluster analysis & Subspace topology. The author has an hindex of 14, co-authored 60 publications receiving 1040 citations. Previous affiliations of Ming Yin include Huazhong University of Science and Technology & Eskişehir Osmangazi University.
Papers
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Journal ArticleDOI
Laplacian Regularized Low-Rank Representation and Its Applications
Ming Yin,Junbin Gao,Zhouchen Lin +2 more
TL;DR: The proposed general Laplacian regularized low-rank representation framework for data representation takes advantage of the graph regularizer and can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data.
Journal ArticleDOI
Multiview Subspace Clustering via Tensorial t-Product Representation
TL;DR: A novel multiview clustering method is proposed by using t-product in the third-order tensor space, to which the tensor-tensor product can be applied and which outperforms the state-of-the-art methods for a range of criteria.
Journal ArticleDOI
Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering
TL;DR: This paper proposes a dual graph regularized LRR model (DGLRR) by enforcing preservation of geometric information in both the ambient space and the feature space and extends the DGLRR model to include non-negative constraint, leading to a parts-based representation of data.
Journal ArticleDOI
Subspace Clustering via Learning an Adaptive Low-Rank Graph.
TL;DR: A novel subspace clustering via learning an adaptive low-rank graph affinity matrix is proposed, where the affinity matrix and the representation coefficients are learned in a unified framework and the pre-computed graph regularizer is effectively obviated and better performance can be achieved.
Proceedings ArticleDOI
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
TL;DR: By embedding the SPD matrices into a Reproducing Kernel Hilbert Space (RKHS), a kernel subspace clustering method is constructed un the SPD manifold through an appropriate Log-Euclidean kernel, termed as kernel sparse sub space clustering on the SPD Riemannian manifold(KSSCR).