Module reduction 

Dimensionality reduction algorithms Dimensionality reduction techniques

This module provides algorithms for reducing the dimensionality of data, which is useful for visualization, feature extraction, and reducing computational complexity.

Structs

Isomap

Isomap (Isometric Feature Mapping) dimensionality reduction

LDA

Linear Discriminant Analysis (LDA) for dimensionality reduction

LLE

Locally Linear Embedding (LLE) dimensionality reduction

PCA

Principal Component Analysis (PCA) dimensionality reduction

SpectralEmbedding

Spectral Embedding dimensionality reduction

TSNE

t-SNE (t-distributed Stochastic Neighbor Embedding) for dimensionality reduction

TruncatedSVD

Truncated Singular Value Decomposition (SVD) for dimensionality reduction

UMAP

UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction

Enums

AffinityMethod

Affinity matrix construction methods

Functions

trustworthiness

Calculate trustworthiness score for a dimensionality reduction