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Multi-view Clustering via Deep Matrix Factorization and Partition Alignment

https://doi.org/10.1145/3474085.3475548
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.inffus.2019.06.017openalex-snapshot
Subspace segmentation-based robust multiple kernel clustering
inferredcites →https://doi.org/10.1016/j.inffus.2020.10.013openalex-snapshot
Relaxed multi-view clustering in latent embedding space
inferredcites →https://doi.org/10.1016/j.inffus.2020.12.007openalex-snapshot
Multilevel projections with adaptive neighbor graph for unsupervised multi-view feature selection
inferredcites →https://doi.org/10.1016/j.knosys.2018.05.017openalex-snapshot
Self-weighted multi-view clustering with soft capped norm
inferredcites →https://doi.org/10.1016/j.neunet.2017.02.003openalex-snapshot
Multi-view clustering via multi-manifold regularized non-negative matrix factorization
inferredcites →https://doi.org/10.1016/j.neunet.2018.08.011openalex-snapshot
Multi-view clustering on unmapped data via constrained non-negative matrix factorization
inferredcites →https://doi.org/10.1016/j.neunet.2019.10.010openalex-snapshot
Partition level multiview subspace clustering
inferredcites →https://doi.org/10.1016/j.patcog.2019.107015openalex-snapshot
Auto-weighted multi-view clustering via deep matrix decomposition
inferredcites →https://doi.org/10.1109/ccdc49329.2020.9163943openalex-snapshot
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss
inferredcites →https://doi.org/10.1109/icdm.2004.10095openalex-snapshot
Multi-View Clustering
inferredcites →https://doi.org/10.1109/icdm.2012.43openalex-snapshot
Kernel-Based Weighted Multi-view Clustering
inferredcites →https://doi.org/10.1109/icip.2015.7351455openalex-snapshot
Feature extraction via multi-view non-negative matrix factorization with local graph regularization
inferredcites →https://doi.org/10.1109/tcyb.2017.2747400openalex-snapshot
Diverse Non-Negative Matrix Factorization for Multiview Data Representation
inferredcites →https://doi.org/10.1109/tcyb.2018.2842052openalex-snapshot
Multiview Clustering Based on Non-Negative Matrix Factorization and Pairwise Measurements
inferredcites →https://doi.org/10.1109/tcyb.2020.2984552openalex-snapshot
Uniform Distribution Non-Negative Matrix Factorization for Multiview Clustering
inferredcites →https://doi.org/10.1109/tkde.2019.2903810openalex-snapshot
GMC: Graph-Based Multi-View Clustering
inferredcites →https://doi.org/10.1109/tmm.2020.2965482openalex-snapshot
Robust Visual Tracking via Constrained Multi-Kernel Correlation Filters
inferredcites →https://doi.org/10.1109/tmm.2020.2975394openalex-snapshot
Learning Adaptive Neighborhood Graph on Grassmann Manifolds for Video/Image-Set Subspace Clustering
inferredcites →https://doi.org/10.1109/tmm.2020.3019683openalex-snapshot
Unsupervised Multi-View Clustering by Squeezing Hybrid Knowledge From Cross View and Each View
inferredcites →https://doi.org/10.1109/tpami.2008.277openalex-snapshot
Convex and Semi-Nonnegative Matrix Factorizations
inferredcites →https://doi.org/10.1109/tpami.2010.231openalex-snapshot
Graph Regularized Nonnegative Matrix Factorization for Data Representation
inferredcites →https://doi.org/10.1137/1.9781611972757.70openalex-snapshot
On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering
inferredcites →https://doi.org/10.1145/1150402.1150420openalex-snapshot
Orthogonal nonnegative matrix t-factorizations for clustering
inferredcites →https://doi.org/10.1145/1273496.1273642openalex-snapshot
Spectral clustering and transductive learning with multiple views
inferredcites →https://doi.org/10.1155/2019/6084382openalex-snapshot
Multiview Clustering via Robust Neighboring Constraint Nonnegative Matrix Factorization
inferredcites →https://doi.org/10.1162/neco_a_01055openalex-snapshot
Adaptive Structure Concept Factorization for Multiview Clustering
inferredcites →https://doi.org/10.1609/aaai.v31i1.10867openalex-snapshot
Multi-View Clustering via Deep Matrix Factorization
inferredcites →https://doi.org/10.1609/aaai.v32i1.11617openalex-snapshot
Consistent and Specific Multi-View Subspace Clustering
inferredcites →https://doi.org/10.24963/ijcai.2019/524openalex-snapshot
Multi-view Clustering via Late Fusion Alignment Maximization

What points at this work

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

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One-Step Multi-View Spectral Clustering Via Subspaces Fusion on Grassmann Manifold
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Visible-Hidden Multi-view Collaborative Fuzzy Clustering Based on Graph Constraints
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Consider high-order consistency for multi-view clustering
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A deep low-rank semantic factorization method for micro-video multi-label classification
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Contrastive supervision and denoising gated fusion for masked graph-based multimodal emotion recognition
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Joint entropy minimization-based statistical dependency for deep multi-view clustering
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Diversity-induced consensus and structured graph learning for multi-view clustering
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Spectral type subspace clustering methods: multi-perspective analysis
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Comprehensive deep contrastive manifold regularized non-negative matrix factorization for multi-view clustering
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Multiview nonnegative matrix factorization with dual HSIC constraints for clustering
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Representation Learning in Multi-view Clustering: A Literature Review
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Weight consistency and cluster diversity based concept factorization for multi-view clustering
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Deep low-rank tensor embedding for multi-view subspace clustering
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A multi-view clustering algorithm based on deep semi-NMF
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A Bottleneck Network with Light Attention for Multimodal Clustering
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DCCNMF: Deep Complementary and Consensus Non-negative Matrix Factorization for multi-view clustering
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Robust anchor-based multi-view clustering via spectral embedded concept factorization
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Local residual preserving non-negative matrix factorization for multi-view clustering
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One-step Multi-view Spectral Clustering with Subspaces Fusion on Grassmann manifold
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Multi-view subspace clustering via adaptive graph learning and late fusion alignment
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Learning deep representation and discriminative features for clustering of multi-layer networks
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A survey on representation learning for multi-view data
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UNAGI: Unified neighbor-aware graph neural network for multi-view clustering
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Nonlinear multi-view clustering for non-negative matrix factorization
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Deep matrix factorization with adaptive weights for multi-view clustering
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Deep Multi-View Clustering via View-Specific Representation and Global Graph
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Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering
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Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase
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Residual-Aware Tensor Regularization for Multi-View Subspace Clustering
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Clustering Algorithms with Balanced Weights for Geographic Data Processing
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Masked Graph Learning With Recurrent Alignment for Multimodal Emotion Recognition in Conversation
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Local High-Order Graph Learning for Multi-View Clustering
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A Multi-Kernel-Based Multi-View Deep Non-Negative Matrix Factorization for Enhanced Healthcare Data Clustering
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Robust Deep Matrix Factorization With Low-Rank and Hypergraph Learning for Multi-View Data Processing
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Diversity Embedding Deep Optimal Graph Regularized Nonnegative Matrix Factorization for Robust Multiview Clustering
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Semantic Alignment Network for Multi-Modal Emotion Recognition
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Symmetric Multi-View Subspace Clustering With Automatic Neighbor Discovery
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Conditional Dual Diffusion for Multimodal Clustering of Optical and SAR Images
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Tensorized Tri-Factor Decomposition for Multi-View Clustering
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Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering
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An Information Theoretical Multi-View Clustering With Graph Structural Information Distance
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Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance
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Fast Self-Guided Multi-View Subspace Clustering
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Bi-Nuclear Tensor Schatten-p Norm Minimization for Multi-View Subspace Clustering
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Multi-View Clustering via Triplex Information Maximization
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Multi-View Scholar Clustering With Dynamic Interest Tracking
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Concept Factorization Based Multiview Clustering for Large-Scale Data
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Learnable Multi-View Matrix Factorization With Graph Embedding and Flexible Loss
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Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering
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Multi-View Clustering Based on Deep Non-negative Tensor Factorization
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