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Self-weighted Multiview Clustering with Multiple Graphs

https://doi.org/10.24963/ijcai.2017/357
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Clustering with Multiple Graphs
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Multi-view clustering ensembles
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Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition
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The Constrained Laplacian Rank Algorithm for Graph-Based Clustering

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Type of Study:Original Papertitle:Structured Multi-View K-Means Clusteringauthor Names and Affiliations:Zitong Zhang (Email: 1810272071@Email.Szu.Edu.Cn)(College of Computer Science and Software, Shenzhen University, Shenzhen, 518060, P.R. China.) Xiaojun Chen* (Email: Xjchen@Szu.Edu.Cn)(College…
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Multi-view transfer learning with privileged learning framework
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Auto-weighted Multi-view learning for Semi-Supervised graph clustering
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Diversity and consistency learning guided spectral embedding for multi-view clustering
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Spectral representation learning for one-step spectral rotation clustering
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Robust multi-view clustering via inter-and-intra-view low rank fusion
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Self-paced and auto-weighted multi-view clustering
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Multi-view spectral clustering via sparse graph learning
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Auto-weighted multi-view clustering via spectral embedding
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An overview of recent multi-view clustering
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Multi-view clustering based on generalized low rank approximation
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Dual distance adaptive multiview clustering
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Multi-view clustering with interactive mechanism
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Self-weighted graph learning for multi-view clustering
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Low-rank constraint bipartite graph learning
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Dissimilarity-based indicator graph learning for clustering
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Side-constrained graph fusion for semi-supervised multi-view clustering
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Supervised adaptive similarity consistent latent representation hashing
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Adaptive dual graph regularization for clustered multi-task learning
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Learning consensus representations in multi-latent spaces for multi-view clustering
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Scalable and parameter-free fusion graph learning for multi-view clustering
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Consensus local graph for multiple kernel clustering
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Auto-weighted multi-view clustering via hierarchical bipartite graph
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Self-paced learning for anchor-based multi-view clustering: A progressive approach
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ERL-ASC: Entropy-regularized logarithmic adaptive spectral clustering for multivariate time series
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Low-rank tensor constrained co-regularized multi-view spectral clustering
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Dual self-paced multi-view clustering
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Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation
inferred← citeshttps://doi.org/10.1016/j.neunet.2022.05.024openalex-snapshot
MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks
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Nonconvex low-rank tensor approximation with graph and consistent regularizations for multi-view subspace learning
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Multi-view subspace clustering via adaptive graph learning and late fusion alignment
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Balance guided incomplete multi-view spectral clustering
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Towards a unified framework for graph-based multi-view clustering
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Composite attention mechanism network for deep contrastive multi-view clustering
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A survey on representation learning for multi-view data
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Partially multi-view clustering via re-alignment
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UNAGI: Unified neighbor-aware graph neural network for multi-view clustering
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Structure regularized consensus dynamic anchor graph learning for incomplete multi-view clustering
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Anchor Graph Learning with Double Noise Removal for Multi-View Clustering
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Dynamic auto-weighted multi-view co-clustering
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Auto-weighted multi-view co-clustering via fast matrix factorization
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Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix
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Robust kernelized graph-based learning
inferred← citeshttps://doi.org/10.1016/j.patcog.2020.107676openalex-snapshot
Discriminative subspace matrix factorization for multiview data clustering
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Incomplete multiview nonnegative representation learning with multiple graphs
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Self-weighting multi-view spectral clustering based on nuclear norm
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Deep multi-view spectral clustering via ensemble
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Parameter-free ensemble clustering with dynamic weighting mechanism
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Multi-view clustering via dynamic unified bipartite graph learning
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Structured multi-view k-means clustering
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Debiasing weighted multi-view k-means clustering based on causal regularization
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Efficient multi-view discrete co-clustering with learned graph
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Locality-driven flexible consensus graph learning for multi-view clustering
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Reliable continual multi-modal clustering
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Self-weighted Multi-view Subspace Clustering With Low Rank Tensor Constraint
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