Namespace AiDotNet.Preprocessing.DimensionalityReduction
Classes
- DictionaryLearning<T>
Dictionary Learning for sparse representation.
- DiffusionMaps<T>
Diffusion Maps for nonlinear dimensionality reduction.
- FactorAnalysis<T>
Factor Analysis for dimensionality reduction with noise modeling.
- FastICA<T>
Fast Independent Component Analysis (FastICA) for blind source separation.
- HessianLLE<T>
Hessian Locally Linear Embedding for nonlinear dimensionality reduction.
- IncrementalPCA<T>
Incremental Principal Component Analysis for large datasets.
- Isomap<T>
Isomap (Isometric Mapping) for nonlinear dimensionality reduction.
- KernelPCA<T>
Kernel Principal Component Analysis for non-linear dimensionality reduction.
- LTSA<T>
Local Tangent Space Alignment for nonlinear dimensionality reduction.
- LaplacianEigenmaps<T>
Laplacian Eigenmaps for nonlinear dimensionality reduction.
- LargeVis<T>
LargeVis for large-scale visualization and dimensionality reduction.
- LatentDirichletAllocation<T>
Latent Dirichlet Allocation for topic modeling.
- LocallyLinearEmbedding<T>
Locally Linear Embedding for nonlinear dimensionality reduction.
- MDS<T>
Multidimensional Scaling for dimensionality reduction.
- MiniBatchSparsePCA<T>
Mini-batch Sparse PCA using online dictionary learning.
- ModifiedLLE<T>
Modified Locally Linear Embedding with regularization.
- NCA<T>
Neighborhood Components Analysis (NCA) for supervised dimensionality reduction.
- NMF<T>
Non-negative Matrix Factorization for dimensionality reduction.
- PCA<T>
Principal Component Analysis for dimensionality reduction.
- PHATE<T>
PHATE: Potential of Heat-diffusion for Affinity-based Transition Embedding.
- PaCMAP<T>
PaCMAP: Pairwise Controlled Manifold Approximation.
- ProbabilisticPCA<T>
Probabilistic Principal Component Analysis (PPCA).
- RandomProjection<T>
Random Projection for dimensionality reduction.
- RandomizedPCA<T>
Randomized PCA using randomized SVD for efficient computation.
- SparsePCA<T>
Sparse Principal Component Analysis using L1 regularization.
- SpectralEmbedding<T>
Spectral Embedding for nonlinear dimensionality reduction.
- TSNE<T>
t-Distributed Stochastic Neighbor Embedding for visualization.
- TriMAP<T>
TriMAP: Large-scale Dimensionality Reduction Using Triplets.
- TruncatedSVD<T>
Truncated Singular Value Decomposition for dimensionality reduction.
- UMAP<T>
Uniform Manifold Approximation and Projection for dimensionality reduction.
Enums
- DictionaryFitAlgorithm
Specifies the algorithm for fitting the dictionary.
- FactorRotation
Specifies the rotation method for Factor Analysis.
- ICAAlgorithm
Specifies the ICA algorithm type.
- ICAFunction
Specifies the non-linearity function for ICA.
- IsomapNeighborAlgorithm
Specifies the neighbor search algorithm for Isomap.
- IsomapPathAlgorithm
Specifies the shortest path algorithm for Isomap.
- KernelType
Specifies the kernel type for Kernel PCA.
- LLEMethod
Specifies the LLE algorithm variant.
- LaplacianAffinityType
Specifies the affinity type for Laplacian Eigenmaps.
- LdaLearningMethod
Specifies the learning method for LDA.
- MDSMetric
Specifies the distance metric for MDS.
- MDSType
Specifies the type of MDS algorithm.
- NMFInit
Specifies the initialization method for NMF.
- NMFSolver
Specifies the solver for NMF optimization.
- RandomProjectionType
Specifies the type of random projection.
- SparseCodingAlgorithm
Specifies the algorithm for sparse coding (transform).
- SpectralAffinity
Specifies how to construct the affinity matrix for Spectral Embedding.
- TSNEInitialization
Specifies the initialization method for t-SNE.
- TSNEMetric
Specifies the distance metric for t-SNE.
- UMAPMetric
Specifies the distance metric for UMAP.