Multi-view learning methods with code（多视角学习的代码集合）
转载请标注，这项收集工作来自广东工业大学梁博士的项目：https://github.com/liangnaiyao/multiview_learning。（此次转载已受到梁博的授权）
博主会及时更新内容，新增文章与代码链接，非常非常欢迎大家挂出自己在多视角领域的研究工作与代码，有任何问题，请私信评论或联系[email protected]，我们非常期待合作。
Multi-view learning methods with code
Part A: general multi-view methods with code
1. NMF (non-negative matrix factorization) based methods
NMF factorizes the non-negative data matrix into two non-negative matrices.
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1.1 AAAI17 Multi-View Clustering via Deep Matrix Factorization (matlab)
- Deep Matrix Factorization is a variant of NMF.
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1.2 ICPR16 Partial Multi-View Clustering Using Graph Regularized NMF (matlab)
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1.3 ICDM16 Multi-View Clustering via Concept Factorization with Local Manifold Regularization (matlab)
- Concept Factorization is a variant of NMF.
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1.4 TC19 Individuality- and Commonality-Based Multiview Multilabel Learning (matlab)
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1.5 AAAI14 Partial Multi-View Clustering (matlab)
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1.6 TNNLS15 Partially Shared Latent Factor Learning With Multiview Data (matlab)
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1.7 S18 Multi-view Discriminative Learning via Joint Non-negative Matrix Factorization (matlab)
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1.8 ICDM13 Multi-View Clustering via Joint Nonnegative Matrix Factorization (matlab)
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1.9 KBS20 Multi-view clustering by non-negative matrix factorization with co-orthogonal constraints (matlab)
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1.10 KBS20 Semi-supervised Multi-view Clustering with Graph-regularized Partially Shared Non-negative Matrix Factorization (matlab)
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1.11 NC18 Adaptive Structure Concept Factorization for Multiview Clustering (matlab)
- Concept Factorization is a variant of NMF.
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1.12 ICDE20 A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering (matlab)
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1.13 ECCV20 SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning (python)
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1.14 PR20 Auto-weighted Multi-view Clustering via Deep Matrix Decomposition (matlab)
2. Graph based methods
It contains two kinds of methods. The first kind is using a predefined graph (also resfer to the traditional spectral clustering), and performing post-processing spectral clustering or k-means. And the second kind is to learn the graph and the index matrix simultaneously.
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2.1 ICDM19 Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering (matlab)
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2.2 TIP19 Multiview Consensus Graph Clustering (matlab)
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2.3 TIP18 Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification (matlab)(python)
- The conference variant is AAAI17 Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours.
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2.4 TKDE19 GMC Graph-based Multi-view Clustering (matlab)
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2.5 BD17 Multi-View Graph Learning with Adaptive Label Propagation (matlab)
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2.6 TC18 Graph Learning for Multiview Clustering (matlab)
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2.7 IJCAI16 Parameter-Free Auto-Weighted Multiple Graph Learning (matlab)
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2.8 TC18 Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (matlab)
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2.9 TKDE19 Graph structure fusion for multiview clustering (matlab)
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2.10 ACML19 Latent Multi-view Semi-Supervised Classification (matlab)
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2.11 NN20 Partition level multiview subspace clustering (matlab)
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2.12 KBS20 Multi-graph Fusion for Multi-view Spectral Clustering (matlab)
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2.13 TIP17 Flexible Multi-view Dimensionality co-Reduction (matlab)
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2.14 ICML19 COMIC: Multi-view Clustering Without Parameter Selection (python)
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2.15 AAAI20 Multi-View Clustering in Latent Embedding Space (matlab)
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2.16 PR19 Multi-view Subspace Clustering with Intactness-Aware Similarity (matlab)
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2.17 IF20 Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding (matlab)
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2.18 N19 Auto-weighted multi-view constrained spectral clustering (matlab)
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2.19 KBS19 A Study of Graph-based System for Multi-view Clustering (matlab)
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2.20 PR19 Auto-weighted Multi-view Clustering via Kernelized Graph Learning (matlab)
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2.21 TKDE21 Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-view Clustering (matlab)
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2.22 IJCAI21 Graph Filter-based Multi-view Attributed Graph Clustering (python)
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2.23 TCYB21 Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view (matlab)
3. Self-representation based methods
Self-representation means that each data sample is expressed by a linear combination of other samples in the same subspace.
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3.1 AAAI18 Consistent and Specific Multi-View Subspace Clustering (matlab)
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3.2 The method in 2.8 is also a self-representation based method.
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3.3 PR18 Multi-view Low-rank Sparse Subspace Clustering (matlab)
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3.4 CVPR15 Diversity-induced Multi-view Subspace Clustering (matlab)
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3.5 TIP19 Split Multiplicative Multi-view Subspace Clustering (matlab)
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3.6 CVPR17 Exclusivity-Consistency Regularized Multi-view Subspace Clustering (matlab)
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3.7 TPAMI20 Generalized Latent Multi-view Subspace Clustering (matlab)
- The conference variant is CVPR17 Latent Multi-view Subspace Clustering.
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3.8 IS21 Multi-view Subspace Clustering via Partition Fusion (matlab)
4. Tensor based methods
The tensor is the generalization of the matrix concept. And the matrix case is a 2-order tensor.
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4.1 TNNLS18 Multiview Subspace Clustering via Tensorial t-Product Representation (matlab)
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4.2 ICCV15 Low-Rank Tensor Constrained Multiview Subspace Clustering (matlab)
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4.3 TIP19 Essential tensor learning for multi-view spectral clustering (matlab)
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4.4 IJCV20 Tensorized Multi-View Subspace Representation Learning (matlab)
- The conference variant may be ICCV15 Low-Rank Tensor Constrained Multiview Subspace Clustering.
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4.5 IJCV18 On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (matlab)
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4.6 TCYB20 Hyper-Laplacian Regularized Multilinear Multi-View Self-Representation for Clustering and Semi-supervised Learning (matlab)
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4.7 TCSVT21 Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation (matlab)
5. Kernel learning based methods
- 5.1 N18 Local kernel alignment based multi-view clustering using extreme learning machine (matlab)
6. Dictionary learning based methods
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6.1 Access18 Multi-view Analysis Dictionary Learning for Image Classification (matlab)
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6.2 TIP16 Multimodal Task-Driven Dictionary Learning for Image Classification(matlab)
7. Deep learning based or network based methods
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7.1 TIP19 Multi-view Deep Subspace Clustering Networks (python)
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7.2 NIPS19 CPM-Nets: Cross Partial Multi-View Networks (python)
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7.3 AAA18 Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (python)
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7.4 TKDE20 MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation (python)
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7.5 TIP19 Multi-View Linear Discriminant Analysis Network (python)
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7.6 TIP19 Deep Multi-View Learning Using Neuron-Wise Correlation-Maximizing Regularizers (python)
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7.7 ICCV15 Multi-view Convolutional Neural Networks for 3D Shape Recognition (matlab)
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7.8 CVPR19 AE2-Nets:Autoencoder in Autoencoder Networks (python)
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7.9 IJCAI19 Multi-view Spectral Clustering Network (python)
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7.10 ICCV19 Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering (matlab)
8. SVM based methods
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8.1 TNNLS18 Multiview Privileged Support Vector Machines (matlab)
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8.2 KBS18 Multi-view learning based on Nonparallel Support Vector Machine (matlab)
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8.3 IS19 Coupling Privileged Kernel Method for Multi-view Learning (matlab)
9. Co-training based methods
- 9.1 JMLR20 Self-paced Multi-view Co-training (python)
10. Metric Learning based methods
- 10.1 IJCAI18 FISH-MML: Fisher-HSIC Multi-View Metric Learning(matlab)
11. Self-supervised Learning
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11.1 ICLR21 Self-supervised Learning from a Multi-view Perspective (python)
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11.2 ECCV20 Contrastive Multiview Coding (python)
Part B: multi-view applications with code
1. Incomplete or partial multi-view learning
Some views of samples are missing.
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1.1 AAAI19 Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering (matlab)
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1.2 ECML15 Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2,1 Regularization (matlab)
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1.3 BD16 Online Multi-view Clustering with Incomplete Views (matlab)
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1.4 IJCAI16 Incomplete Multi-Modal Visual Data Grouping (matlab)
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1.5 TC20 Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion (matlab)
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1.6 IJCAI19 Spectral Perturbation Meets Incomplete Multi-view Data (matlab)
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1.7 TPAMI, in press, Deep Partial Multi-View Learning (python)
- The conference variant is NIPS19 CPM-Nets: Cross Partial Multi-View Networks.
2. Person Re-Identification
- 2.1 TPAMI18 Person Re-Identification by Cross-View Multi-Level Dictionary Learning (matlab)
3. Outlier detection
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3.1 TKDD18 Multi-View Low-Rank Analysis with Applications to Outlier Detection (matlab)
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3.2 AAAI18 Partial Multi-View Outlier Detection (matlab)
4. Zero shot learning
- 4.1 ECCV14 Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation (matlab)
5. Multi-label learning or Weak-label learning
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5.1 SIAM SDM18 Multi-view Weak-label Learning based on Matrix Completion (matlab)
- Weak-label learning is an important branch of multi-label learning.
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5.2 Access19 Multi-View Multi-Label Learning With View-Label-Specific Features (matlab)
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5.3 The method in 1.4 is also a multi-label learning method.
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5.4 IJCAI18 Incomplete Multi-View Weak-Label Learning (matlab)
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5.5 IJCAI20 Weakly-Supervised Multi-view Multi-instance Multi-label Learning (matlab)
6. Online learning
- 6.1 ICDM16 Online Unsupervised Multi-view Feature Selection (matlab)
7. Multi-Instance learning
- 7.1 AAA19 Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization (matlab)
8. Large-scale clustering
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8.1 AAAI20 Large-scale Multi-view Subspace Clustering in Linear Time (matlab)
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8.2 AAAI15 Large-scale multi-view spectral clustering via bipartite graph (matlab)
9. Non-independently and Non-identically Distributed Complex Noise
- 9.1 TNNLS19 Robust Multi-view Subspace Learning with Non-independently and Non-identically Distributed Complex Noise (matlab)
10. Multiview training boost Single-view test
- 10.1 TPAMI20 Multiview Feature Selection for Single-view Classification (matlab)
Part C: Others
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1.1 famous authors in the field of multi-view learning
- Feiping Nie (Google Scholar Citations) (home)
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1.2 other github pages with code