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      "page": "linear_ELDE",
      "title": "Exponential Local Discriminant Embedding",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.elde"
      ]
    },
    {
      "page": "linear_ELPP2",
      "title": "Enhanced Locality Preserving Projection (2013)",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.elpp2"
      ]
    },
    {
      "page": "feature_ENET",
      "title": "Elastic Net Regularization",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.enet"
      ]
    },
    {
      "page": "linear_ESLPP",
      "title": "Extended Supervised Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.eslpp"
      ]
    },
    {
      "page": "linear_EXTLPP",
      "title": "Extended Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.extlpp"
      ]
    },
    {
      "page": "linear_FA",
      "title": "Exploratory Factor Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.fa"
      ]
    },
    {
      "page": "nonlinear_FastMap",
      "title": "FastMap",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.fastmap"
      ]
    },
    {
      "page": "feature_FOSMOD",
      "title": "Forward Orthogonal Search by Maximizing the Overall Dependency",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.fosmod"
      ]
    },
    {
      "page": "feature_FSCORE",
      "title": "Fisher Score",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.fscore"
      ]
    },
    {
      "page": "linear_FSSEM",
      "title": "Feature Subset Selection using Expectation-Maximization",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.fssem"
      ]
    },
    {
      "page": "nonlinear_HYDRA",
      "title": "Hyperbolic Distance Recovery and Approximation",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.hydra"
      ]
    },
    {
      "page": "linear_ICA",
      "title": "Independent Component Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.ica"
      ]
    },
    {
      "page": "nonlinear_IDMAP",
      "title": "Interactive Document Map",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.idmap"
      ]
    },
    {
      "page": "nonlinear_ILTSA",
      "title": "Improved Local Tangent Space Alignment",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.iltsa"
      ]
    },
    {
      "page": "nonlinear_ISOMAP",
      "title": "Isometric Feature Mapping",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.isomap"
      ]
    },
    {
      "page": "linear_ISOPROJ",
      "title": "Isometric Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.isoproj"
      ]
    },
    {
      "page": "nonlinear_ISPE",
      "title": "Isometric Stochastic Proximity Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.ispe"
      ]
    },
    {
      "page": "nonlinear_KECA",
      "title": "Kernel Entropy Component Analysis",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.keca"
      ]
    },
    {
      "page": "nonlinear_KLDE",
      "title": "Kernel Local Discriminant Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.klde"
      ]
    },
    {
      "page": "nonlinear_KLFDA",
      "title": "Kernel Local Fisher Discriminant Analysis",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.klfda"
      ]
    },
    {
      "page": "nonlinear_KLSDA",
      "title": "Kernel Locality Sensitive Discriminant Analysis",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.klsda"
      ]
    },
    {
      "page": "nonlinear_KMFA",
      "title": "Kernel Marginal Fisher Analysis",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.kmfa"
      ]
    },
    {
      "page": "nonlinear_KMMC",
      "title": "Kernel Maximum Margin Criterion",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.kmmc"
      ]
    },
    {
      "page": "linear_KMVP",
      "title": "Kernel-Weighted Maximum Variance Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.kmvp"
      ]
    },
    {
      "page": "nonlinear_KPCA",
      "title": "Kernel Principal Component Analysis",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.kpca"
      ]
    },
    {
      "page": "nonlinear_KQMI",
      "title": "Kernel Quadratic Mutual Information",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.kqmi"
      ]
    },
    {
      "page": "nonlinear_KSDA",
      "title": "Kernel Semi-Supervised Discriminant Analysis",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.ksda"
      ]
    },
    {
      "page": "linear_KUDP",
      "title": "Kernel-Weighted Unsupervised Discriminant Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.kudp"
      ]
    },
    {
      "page": "nonlinear_LAMP",
      "title": "Local Affine Multidimensional Projection",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.lamp"
      ]
    },
    {
      "page": "nonlinear_LAPEIG",
      "title": "Laplacian Eigenmaps",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.lapeig"
      ]
    },
    {
      "page": "feature_LASSO",
      "title": "Least Absolute Shrinkage and Selection Operator",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.lasso"
      ]
    },
    {
      "page": "linear_LDA",
      "title": "Linear Discriminant Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lda"
      ]
    },
    {
      "page": "linear_LDAKM",
      "title": "Combination of LDA and K-means",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.ldakm"
      ]
    },
    {
      "page": "linear_LDE",
      "title": "Local Discriminant Embedding",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lde"
      ]
    },
    {
      "page": "linear_LDP",
      "title": "Locally Discriminating Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.ldp"
      ]
    },
    {
      "page": "linear_LEA",
      "title": "Locally Linear Embedded Eigenspace Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lea"
      ]
    },
    {
      "page": "linear_LFDA",
      "title": "Local Fisher Discriminant Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lfda"
      ]
    },
    {
      "page": "nonlinear_LISOMAP",
      "title": "Landmark Isometric Feature Mapping",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.lisomap"
      ]
    },
    {
      "page": "nonlinear_LLE",
      "title": "Locally Linear Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.lle"
      ]
    },
    {
      "page": "nonlinear_LLLE",
      "title": "Local Linear Laplacian Eigenmaps",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.llle"
      ]
    },
    {
      "page": "linear_LLP",
      "title": "Local Learning Projections",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.llp"
      ]
    },
    {
      "page": "linear_LLTSA",
      "title": "Linear Local Tangent Space Alignment",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lltsa"
      ]
    },
    {
      "page": "linear_LMDS",
      "title": "Landmark Multidimensional Scaling",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lmds"
      ]
    },
    {
      "page": "linear_LPCA2006",
      "title": "Locally Principal Component Analysis by Yang et al. (2006)",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lpca2006"
      ]
    },
    {
      "page": "linear_LPE",
      "title": "Locality Pursuit Embedding",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lpe"
      ]
    },
    {
      "page": "linear_LPFDA",
      "title": "Locality Preserving Fisher Discriminant Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lpfda"
      ]
    },
    {
      "page": "linear_LPMIP",
      "title": "Locality-Preserved Maximum Information Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lpmip"
      ]
    },
    {
      "page": "linear_LPP",
      "title": "Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lpp"
      ]
    },
    {
      "page": "linear_LQMI",
      "title": "Linear Quadratic Mutual Information",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lqmi"
      ]
    },
    {
      "page": "feature_LSCORE",
      "title": "Laplacian Score",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.lscore"
      ]
    },
    {
      "page": "linear_LSDA",
      "title": "Locality Sensitive Discriminant Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lsda"
      ]
    },
    {
      "page": "feature_LSDF",
      "title": "Locality Sensitive Discriminant Feature",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.lsdf"
      ]
    },
    {
      "page": "linear_LSIR",
      "title": "Localized Sliced Inverse Regression",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lsir"
      ]
    },
    {
      "page": "feature_LSLS",
      "title": "Locality Sensitive Laplacian Score",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.lsls"
      ]
    },
    {
      "page": "feature_LSPE",
      "title": "Locality and Similarity Preserving Embedding",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.lspe"
      ]
    },
    {
      "page": "linear_LSPP",
      "title": "Local Similarity Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.lspp"
      ]
    },
    {
      "page": "nonlinear_LTSA",
      "title": "Local Tangent Space Alignment",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.ltsa"
      ]
    },
    {
      "page": "feature_MCFS",
      "title": "Multi-Cluster Feature Selection",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.mcfs"
      ]
    },
    {
      "page": "linear_MDS",
      "title": "(Classical) Multidimensional Scaling",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.mds"
      ]
    },
    {
      "page": "linear_MFA",
      "title": "Marginal Fisher Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.mfa"
      ]
    },
    {
      "page": "feature_MIFS",
      "title": "Mutual Information for Selecting Features",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.mifs"
      ]
    },
    {
      "page": "linear_MLIE",
      "title": "Maximal Local Interclass Embedding",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.mlie"
      ]
    },
    {
      "page": "linear_MMC",
      "title": "Maximum Margin Criterion",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.mmc"
      ]
    },
    {
      "page": "nonlinear_MMDS",
      "title": "Metric Multidimensional Scaling",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.mmds"
      ]
    },
    {
      "page": "linear_MMP",
      "title": "Maximum Margin Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.mmp"
      ]
    },
    {
      "page": "linear_MMSD",
      "title": "Multiple Maximum Scatter Difference",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.mmsd"
      ]
    },
    {
      "page": "linear_MODP",
      "title": "Modified Orthogonal Discriminant Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.modp"
      ]
    },
    {
      "page": "linear_MSD",
      "title": "Maximum Scatter Difference",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.msd"
      ]
    },
    {
      "page": "nonlinear_MVE",
      "title": "Minimum Volume Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.mve"
      ]
    },
    {
      "page": "linear_MVP",
      "title": "Maximum Variance Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.mvp"
      ]
    },
    {
      "page": "nonlinear_MVU",
      "title": "Maximum Variance Unfolding / Semidefinite Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.mvu",
        "do.sde"
      ]
    },
    {
      "page": "nonlinear_NNP",
      "title": "Nearest Neighbor Projection",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.nnp"
      ]
    },
    {
      "page": "linear_NOLPP",
      "title": "Nonnegative Orthogonal Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.nolpp"
      ]
    },
    {
      "page": "linear_NONPP",
      "title": "Nonnegative Orthogonal Neighborhood Preserving Projections",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.nonpp"
      ]
    },
    {
      "page": "linear_NPCA",
      "title": "Nonnegative Principal Component Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.npca"
      ]
    },
    {
      "page": "linear_NPE",
      "title": "Neighborhood Preserving Embedding",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.npe"
      ]
    },
    {
      "page": "feature_NRSR",
      "title": "Non-convex Regularized Self-Representation",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.nrsr"
      ]
    },
    {
      "page": "linear_ODP",
      "title": "Orthogonal Discriminant Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.odp"
      ]
    },
    {
      "page": "linear_OLDA",
      "title": "Orthogonal Linear Discriminant Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.olda"
      ]
    },
    {
      "page": "linear_OLPP",
      "title": "Orthogonal Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.olpp"
      ]
    },
    {
      "page": "linear_ONPP",
      "title": "Orthogonal Neighborhood Preserving Projections",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.onpp"
      ]
    },
    {
      "page": "linear_OPLS",
      "title": "Orthogonal Partial Least Squares",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.opls"
      ]
    },
    {
      "page": "linear_PCA",
      "title": "Principal Component Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.pca"
      ]
    },
    {
      "page": "feature_PFA",
      "title": "Principal Feature Analysis",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.pfa"
      ]
    },
    {
      "page": "linear_PFLPP",
      "title": "Parameter-Free Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.pflpp"
      ]
    },
    {
      "page": "nonlinear_PHATE",
      "title": "Potential of Heat Diffusion for Affinity-based Transition Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.phate"
      ]
    },
    {
      "page": "nonlinear_PLP",
      "title": "Piecewise Laplacian-based Projection (PLP)",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.plp"
      ]
    },
    {
      "page": "linear_PLS",
      "title": "Partial Least Squares",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.pls"
      ]
    },
    {
      "page": "linear_PPCA",
      "title": "Probabilistic Principal Component Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.ppca"
      ]
    },
    {
      "page": "feature_PROCRUSTES",
      "title": "Feature Selection using PCA and Procrustes Analysis",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.procrustes"
      ]
    },
    {
      "page": "nonlinear_REE",
      "title": "Robust Euclidean Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.ree"
      ]
    },
    {
      "page": "linear_RLDA",
      "title": "Regularized Linear Discriminant Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.rlda"
      ]
    },
    {
      "page": "linear_RNDPROJ",
      "title": "Random Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.rndproj"
      ]
    },
    {
      "page": "nonlinear_RPCA",
      "title": "Robust Principal Component Analysis",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.rpca"
      ]
    },
    {
      "page": "linear_RPCAG",
      "title": "Robust Principal Component Analysis via Geometric Median",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.rpcag"
      ]
    },
    {
      "page": "linear_RSIR",
      "title": "Regularized Sliced Inverse Regression",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.rsir"
      ]
    },
    {
      "page": "feature_RSR",
      "title": "Regularized Self-Representation",
      "concept": [
        "feature_methods"
      ],
      "topics": [
        "do.rsr"
      ]
    },
    {
      "page": "linear_SAMMC",
      "title": "Semi-Supervised Adaptive Maximum Margin Criterion",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.sammc"
      ]
    },
    {
      "page": "nonlinear_SAMMON",
      "title": "Sammon Mapping",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.sammon"
      ]
    },
    {
      "page": "linear_SAVE",
      "title": "Sliced Average Variance Estimation",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.save"
      ]
    },
    {
      "page": "linear_SDA",
      "title": "Semi-Supervised Discriminant Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.sda"
      ]
    },
    {
      "page": "linear_SDLPP",
      "title": "Sample-Dependent Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.sdlpp"
      ]
    },
    {
      "page": "linear_SIR",
      "title": "Sliced Inverse Regression",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.sir"
      ]
    },
    {
      "page": "linear_SLPE",
      "title": "Supervised Locality Pursuit Embedding",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.slpe"
      ]
    },
    {
      "page": "linear_SLPP",
      "title": "Supervised Locality Preserving Projection",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.slpp"
      ]
    },
    {
      "page": "nonlinear_SNE",
      "title": "Stochastic Neighbor Embedding",
      "concept": [
        "nonlinear_methods"
      ],
      "topics": [
        "do.sne"
      ]
    },
    {
      "page": "linear_SPC",
      "title": "Supervised Principal Component Analysis",
      "concept": [
        "linear_methods"
      ],
      "topics": [
        "do.spc"
      ]
    },
    {
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