On the public citation graph
One-Step Multi-View Spectral Clustering Via Subspaces Fusion on Grassmann Manifold
0 signed · 54 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf01581204openalex-extracted
On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators
inferredcites →https://doi.org/10.1007/s10915-016-0182-0openalex-extracted
Iteration Complexity Analysis of Multi-block ADMM for a Family of Convex Minimization Without Strong Convexity
inferredcites →https://doi.org/10.1007/s11263-018-1086-2openalex-extracted
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization
inferredcites →https://doi.org/10.1016/j.inffus.2017.02.007openalex-extracted
Multi-view learning overview: Recent progress and new challenges
inferredcites →https://doi.org/10.1016/j.inffus.2018.02.005openalex-extracted
Generative multi-view and multi-feature learning for classification
inferredcites →https://doi.org/10.1016/j.inffus.2019.09.005openalex-extracted
Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding
inferredcites →https://doi.org/10.1016/j.inffus.2022.10.020openalex-extracted
Seeking commonness and inconsistencies: A jointly smoothed approach to multi-view subspace clustering
inferredcites →https://doi.org/10.1016/j.ins.2020.04.031openalex-extracted
Co-regularized nonnegative matrix factorization for evolving community detection in dynamic networks
inferredcites →https://doi.org/10.1016/j.knosys.2019.105102openalex-extracted
Multi-graph fusion for multi-view spectral clustering
inferredcites →https://doi.org/10.1016/j.neucom.2020.02.104openalex-extracted
An overview of recent multi-view clustering
inferredcites →https://doi.org/10.1016/j.neunet.2019.10.010openalex-extracted
Partition level multiview subspace clustering
inferredcites →https://doi.org/10.1016/j.patcog.2017.08.024openalex-extracted
Multi-view low-rank sparse subspace clustering
inferredcites →https://doi.org/10.1016/j.patcog.2019.107015openalex-extracted
Auto-weighted multi-view clustering via deep matrix decomposition
inferredcites →https://doi.org/10.1109/43.159993openalex-extracted
New spectral methods for ratio cut partitioning and clustering
inferredcites →https://doi.org/10.1109/cvpr.2015.7298657openalex-extracted
Diversity-induced Multi-view Subspace Clustering
inferredcites →https://doi.org/10.1109/cvpr52729.2023.02295openalex-extracted
Sample-level Multi-view Graph Clustering
inferredcites →https://doi.org/10.1109/iccv.2015.482openalex-extracted
Multi-view Subspace Clustering
inferredcites →https://doi.org/10.1109/tai.2021.3065894openalex-extracted
A Survey on Multiview Clustering
inferredcites →https://doi.org/10.1109/tbdata.2021.3128906openalex-extracted
Multi-View Clustering With Self-Representation and Structural Constraint
inferredcites →https://doi.org/10.1109/tcyb.2018.2868742openalex-extracted
Spectral Clustering by Joint Spectral Embedding and Spectral Rotation
inferredcites →https://doi.org/10.1109/tcyb.2018.2869789openalex-extracted
Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning
inferredcites →https://doi.org/10.1109/tip.2016.2553459openalex-extracted
Convex Sparse Spectral Clustering: Single-View to Multi-View
inferredcites →https://doi.org/10.1109/tip.2021.3131941openalex-extracted
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance
inferredcites →https://doi.org/10.1109/tkde.2017.2701825openalex-extracted
Discrete Nonnegative Spectral Clustering
inferredcites →https://doi.org/10.1109/tkde.2018.2832205openalex-extracted
Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization
inferredcites →https://doi.org/10.1109/tkde.2018.2872061openalex-extracted
Graph Structure Fusion for Multiview Clustering
inferredcites →https://doi.org/10.1109/tkde.2018.2873378openalex-extracted
One-Step Multi-View Spectral Clustering
inferredcites →https://doi.org/10.1109/tkde.2019.2903810openalex-extracted
GMC: Graph-Based Multi-View Clustering
inferredcites →https://doi.org/10.1109/tkde.2020.3022072openalex-extracted
Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering
inferredcites →https://doi.org/10.1109/tkde.2020.3045770openalex-extracted
Consensus One-Step Multi-View Subspace Clustering
inferredcites →https://doi.org/10.1109/tkde.2021.3068461openalex-extracted
Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-View Clustering
inferredcites →https://doi.org/10.1109/tkde.2023.3270311openalex-extracted
A Comprehensive Survey on Multi-View Clustering
inferredcites →https://doi.org/10.1109/tnnls.2023.3253246openalex-extracted
Efficient and Effective One-Step Multiview Clustering
inferredcites →https://doi.org/10.1109/tpami.2012.88openalex-extracted
Robust Recovery of Subspace Structures by Low-Rank Representation
inferredcites →https://doi.org/10.1109/tpami.2013.57openalex-extracted
Sparse Subspace Clustering: Algorithm, Theory, and Applications
inferredcites →https://doi.org/10.1109/tpami.2018.2847335openalex-extracted
Binary Multi-View Clustering
inferredcites →https://doi.org/10.1109/tpami.2018.2877660openalex-extracted
Generalized Latent Multi-View Subspace Clustering
inferredcites →https://doi.org/10.1109/tpami.2020.3011148openalex-extracted
Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method
inferredcites →https://doi.org/10.1109/tpami.2022.3187976openalex-extracted
Tensorized Bipartite Graph Learning for Multi-View Clustering
inferredcites →https://doi.org/10.1109/tsp.2013.2295553openalex-extracted
Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds
inferredcites →https://doi.org/10.1145/2623330.2623726openalex-extracted
Clustering and projected clustering with adaptive neighbors
inferredcites →https://doi.org/10.1145/3474085.3475548openalex-extracted
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
inferredcites →https://doi.org/10.1515/9781400830244openalex-extracted
Optimization Algorithms on Matrix Manifolds
inferredcites →https://doi.org/10.1515/jnum-2013-0013openalex-extracted
Angles between subspaces and their tangents
inferredcites →https://doi.org/10.1609/aaai.v27i1.8683openalex-extracted
Spectral Rotation versus K-Means in Spectral Clustering
inferredcites →https://doi.org/10.1609/aaai.v28i1.8950openalex-extracted
Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition
inferredcites →https://doi.org/10.1609/aaai.v30i1.10302openalex-extracted
The Constrained Laplacian Rank Algorithm for Graph-Based Clustering
inferredcites →https://doi.org/10.1609/aaai.v32i1.11617openalex-extracted
Consistent and Specific Multi-View Subspace Clustering
inferredcites →https://doi.org/10.1609/aaai.v34i04.5807openalex-extracted
Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering
inferredcites →https://doi.org/10.24963/ijcai.2017/357openalex-extracted
Self-weighted Multiview Clustering with Multiple Graphs
inferredcites →https://doi.org/10.24963/ijcai.2019/375openalex-extracted
Multiple Partitions Aligned Clustering
inferredcites →https://doi.org/10.24963/ijcai.2019/438openalex-extracted
Robust Flexible Feature Selection via Exclusive L21 Regularization
inferredcites →https://doi.org/10.24963/ijcai.2019/524openalex-extracted
Multi-view Clustering via Late Fusion Alignment Maximization
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.