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『度量学习』知识梳理

  • 奔驰上线娱乐亚洲第一
  • 2019-10-11
  • 394人已阅读
简介graphRLsubgraph0a1[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]--&g
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-informationInformation-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

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