graph RL subgraph 0 a1[度量學習] --> |也稱為馬氏度量學習問題|b1[線性變換] a1[度量學習] --> b2[非線性變換] end subgraph 1 b1 --> c1[監督學習] c1 --> |該類型的算法充分利用數據的標簽信息|d1[全局] c1 --> |該類型的算法同時考慮數據的標簽信息和數據點之間的幾何關系|d2[局部] end subgraph 2 b1 --> c2[非監督學習] 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[非線性降維] 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（多維尺度變換）
- NMF: Non-negative Matrix Factorization（非負矩陣分解）
- ICA: Independent components analysis（獨立成分分析）
- NPE: Neighborhood Preserving Embedding（鄰域保持嵌入）
- LPP: Locality Preserving Projections（局部保留投影）
- ISOMAP: Isometric Mapping（等距映射）
- LLE: Locally Linear Embedding（局部線性嵌入）
- LE: Laplacian Eigenmap（拉普拉斯特征映射）