graph RL
subgraph 0
a1[度量学习] --> |也称为马氏度量学习问题|b1[线性变换]
a1[度量学习] --> b2[非线性变换]
end
subgraph 1
b1 --> c1[监督学习]
c1 --> |该类型的算法充分利用数据的标签信息|d1[全局]
c1 --> |该类型的算法同时考虑数据的标签信息和数据点之间的几何关系|d2[局部]
end
subgraph 2
b1 --> c2[非监督学习]
end
subgraph 3
d1 --> f1[ITML]
d1 --> f2[MMC]
d1 --> f3[MCML]
end
subgraph 4
d2 --> g1[NCA]
d2 --> g2[LMNN]
d2 --> g3[RCA]
d2 --> g4[Local LDA]
end
subgraph 5
c2 --> e1[PCA]
c2 --> e2[MDS]
c2 --> e3[NMF]
c2 --> e4[ICA]
c2 --> e5[NPE]
c2 --> e6[LPP]
end
subgraph 6
b2 --> b3[非线性降维]
b2 --> b4[核方法]
end
subgraph 7
b3 --> h1[ISOMAP]
b3 --> h2[LLE]
b3 --> h3[LE]
end
subgraph 8
b4 --> t1[Non-Mahalanobis Local Distance Functions]
b4 --> t2[Mahalanobis Local Distance Functions]
b4 --> t3[Metric Learning with Neural Networks]
end
- ITML: Information-theoretic metric learning
- MMC: Mahalanobis Metric Learning for Clustering
- MCML: Maximally Collapsing Metric Learning
- NCA: Neighbourhood Components Analysis
- LMNN: Large-Margin Nearest Neighbors
- RCA: Relevant Component Analysis
- Local LDA: Local Linear Discriminative Analysis
- PCA: Pricipal Components Analysis（主成分分析）
- MDS: Multi-dimensional Scaling（多维尺度变换）
- NMF: Non-negative Matrix Factorization（非负矩阵分解）
- ICA: Independent components analysis（独立成分分析）
- NPE: Neighborhood Preserving Embedding（邻域保持嵌入）
- LPP: Locality Preserving Projections（局部保留投影）
- ISOMAP: Isometric Mapping（等距映射）
- LLE: Locally Linear Embedding（局部线性嵌入）
- LE: Laplacian Eigenmap（拉普拉斯特征映射）
几篇经典论文
- Distance metric learning with application to clustering with side-information
- Information-theoretic metric learning(关于ITML)
- Distance metric learning for large margin nearest neighbor classification(关于LMNN)
- Learning the parts of objects by non-negative matrix factorization（Nature关于RCA的文章）
- Neighbourhood components analysis(关于NCA)
- Metric Learning by Collapsing Classes(关于MCML)
- Distance metric learning a comprehensive survey(一篇经典的综述)
Python 封装了一些度量方法：metric-learn