ferrolearn-decomp

Dimensionality reduction and matrix decomposition for the ferrolearn machine learning framework. Validated against scikit-learn 1.8.0 head-to-head — see the workspace BENCHMARKS.md.

Algorithms

Linear methods

Model	Description
`PCA`	Principal Component Analysis via faer self-adjoint eigensolver
`IncrementalPCA`	Out-of-core PCA for large datasets
`TruncatedSVD`	Randomized SVD (Halko algorithm) — works on uncentered/sparse data
`NMF` / `MiniBatchNMF`	Non-negative Matrix Factorization (coordinate descent and multiplicative update solvers)
`SparsePCA`	Sparse PCA via online dictionary learning
`FactorAnalysis`	Factor Analysis via EM algorithm
`FastICA`	Independent Component Analysis (parallel + deflation)
`DictionaryLearning`	Dictionary learning for sparse coding
`LDATopic`	Latent Dirichlet Allocation for topic modelling
`CCA` (cross_decomposition)	Canonical Correlation Analysis & PLS variants

Manifold learning

Model	Description
`KernelPCA`	Non-linear PCA via RBF, polynomial, or sigmoid kernels
`Isomap`	Isometric mapping via geodesic distances on a kNN graph
`MDS`	Classical and metric Multidimensional Scaling
`SpectralEmbedding`	Laplacian Eigenmaps
`LLE`	Locally Linear Embedding (standard + modified)
`Tsne`	t-distributed Stochastic Neighbor Embedding
`Umap`	Uniform Manifold Approximation and Projection

Example

use ferrolearn_decomp::PCA;
use ferrolearn_core::{Fit, Transform};
use ndarray::array;

let x = array![[1.0_f64, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]];

let pca = PCA::<f64>::new(2);
let fitted = pca.fit(&x, &()).unwrap();
let projected = fitted.transform(&x).unwrap();
assert_eq!(projected.ncols(), 2);

// Inspect explained variance
let variance_ratio = fitted.explained_variance_ratio();

sklearn parity note

PCA, IncrementalPCA, TruncatedSVD, and FactorAnalysis produce reconstruction errors numerically identical to scikit-learn (recon_rel ratio = 1.000× across all measured dataset sizes).

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.