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- Guomin ZhangSuggested from co-authorship
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Works315 from public data
- Deep Clustering with Convolutional Autoencoders543
A convolutional autoencoders structure is developed to learn embedded features in an end-to-end way and a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment.
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- Scalable Multi-view Subspace Clustering with Unified Anchors362
This work proposes a Scalable Multi-view subspace Clustering with Unified Anchors (SMVSC), which combines anchor learning and graph construction into a unified optimization framework and achieves comparable or better clustering performance much more efficiently.
- Deep Graph Clustering via Dual Correlation Reduction338
This work proposes a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner and introduces a propagation regularization term to enable the network to gain long-distance information with the shallow network structure.
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- Deep Fusion Clustering Network307
A Deep Fusion Clustering Network (DFCN) is proposed, in which an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoen coder for consensus representation learning.
- Multiple Kernel k-means with Incomplete Kernels300
Two simple yet effective algorithms that perform multiple kernel clustering directly with the presence of incomplete kernel matrices, which are treated as auxiliary variables to be jointly optimized and which consistently achieve superior performance are proposed.
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- Multiple Kernel k-Means Clustering with Matrix-Induced Regularization260
This paper proposes an MKKM clustering with a novel, effective matrix-induced regularization to reduce such redundancy and enhance the diversity of the selected kernels and shows that maximizing the kernel alignment for clustering can be viewed as a special case of this approach.
- Multi-view Clustering via Late Fusion Alignment Maximization257
This paper theoretically uncovers the connection between existing k-means clustering and the alignment between base partitions and consensus partition and proposes a simple but effective multi-view algorithm termed MVC-LFA, which proposes to maximally align the consensus partition with the weighted base partitions.
- CGD: Multi-View Clustering via Cross-View Graph Diffusion231
The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by capturing the underlying manifold geometry structure of original data points, and leveraging the complementary information among multiple graphs.
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- Unsupervised feature selection via multiple graph fusion and feature weight learning190
This paper introduces a novel and effective unsupervised feature selection method via multiple graph fusion and feature weight learning (MGF^2WL) to address these issues, and develops a graph fusion term to fuse multiple predefined similarity graphs for learning a unified similarity graph.
- DealMVC: Dual Contrastive Calibration for Multi-view Clustering189
A novel Dual contrastive calibration network for Multi-View Clustering (DealMVC) is proposed, which first design a fusion mechanism to obtain a global cross-view feature, and proposes a global Contrastive calibration loss to constrain the consistency of pair-wise view features.
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- Deep Incomplete Multi-View Clustering with Cross-View Partial Sample and Prototype Alignment144
A Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering and a prototype alignment module to achieve incomplete distribution calibration across views are proposed.
- One-Step Multi-View Clustering With Diverse Representation141
This work proposes a one-step multi-view clustering with diverse representation (OMVCDR) method, which incorporates multi-view learning and k-means into a unified framework and develops an efficient optimization algorithm with proven convergence to solve the resultant problem.
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- Optimal Neighborhood Kernel Clustering with Multiple Kernels133
An optimal neighborhood kernel clustering (ONKC) algorithm is proposed to enhance the representability of the optimal kernel and strengthen the negotiation between kernel learning and clustering and significantly outperforms the state-of-the-art ones in the literature.
- Improved autoencoder for unsupervised anomaly detection128
Different from previous methods, by integrating the anomaly detection‐based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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