Single-view learning methods with code

Part A: general single-view methods with code

1. NMF (non-negative matrix factorization) based methods

 NMF factorizes the non-negative data matrix into two non-negative matrices.

1.1 ACM15 Robust capped norm nonnegative matrix factorization (matlab)
1.2 AAAI17 Local centroids structured non-negative matrix factorization (matlab)
1.3 TKDD13 Robust Manifold Non-Negative Matrix Factorization (matlab)
1.4 JMLR14 A Deep Semi-NMF Model for Learning Hidden Representations (matlab)
1.5 WASPAA17 Deep recurrent NMF for speech separation by unfolding iterative thresholding (python)

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.

2.1 SIGKDD14 Clustering and Projected Clustering with Adaptive Neighbors (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.

3.1 NN18 Low-rank representation with adaptive graph regularization (matlab)

4. Least square regression methods

4.1 NN18 Inter-class sparsity based discriminative least square regression (matlab)
4.2 IJCAI17 Semi-supervised Orthogonal Graph Embedding with Recursive Projections (matlab)

5. Tensor based methods

 The tensor is the generalization of the matrix concept. And the matrix case is a 2-order tensor.

6. Kernel learning based methods

6. Dictionary learning based methods

7. Deep learning based or network based methods

8. SVM based methods

9. Co-training based methods

Part B: single-view applications with code

1. None

Part C: Others

1.1 famous authors in the field of -view learning
- None

mengkunzhao / single-view_learning Goto Github PK

single-view_learning's Introduction

Single-view learning methods with code

Part A: general single-view methods with code

1. NMF (non-negative matrix factorization) based methods

2. Graph based methods

3. Self-representation based methods

4. Least square regression methods

5. Tensor based methods

6. Kernel learning based methods

6. Dictionary learning based methods

7. Deep learning based or network based methods

8. SVM based methods

9. Co-training based methods

Part B: single-view applications with code

1. None

Part C: Others

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