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『度量學習』知識梳理

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  • 2019-04-25
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簡介graphRLsubgraph0a1[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]--&g
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)

世界杯半全场是什么意思 www.ezveqs.com.cn 幾篇經典論文

Distance metric learning with application to clustering with side-informationInformation-theoretic metric learning(關于ITML)Distance metric learning for large margin nearest neighbor classification(關于LMNN)Learning the parts of objects by non-negative matrix factorization(Nature關于RCA的文章)Neighbourhood components analysis(關于NCA)Metric Learning by Collapsing Classes(關于MCML)Distance metric learning a comprehensive survey(一篇經典的綜述)

Python 封裝了一些度量方法:metric-learn

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