solow-decomposition 0.7.1

Rust matrix decomposition for machine learning: PCA, KernelPCA, FastICA, NMF, MiniBatchNMF, TruncatedSVD (LSA), IncrementalPCA, SparsePCA, DictionaryLearning, MiniBatchDictionaryLearning, LatentDirichletAllocation, GaussianRandomProjection, SparseRandomProjection. Scikit-learn decomposition and random_projection compatible.

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122 out of 122 items documented0 out of 42 items with examples
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Source code size: 89.28 kB This is the summed size of all the files inside the crates.io package for this release.
Documentation size: 1.54 MB This is the summed size of all files generated by rustdoc for all configured targets
Ø build duration
this release: 7s Average build duration of successful builds.
all releases: 13s Average build duration of successful builds in releases after 2024-10-23.
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benovamurat/solow
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crates.io
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Versions
- 0.7.3 (2026-09-05)
- 0.7.1 (2026-09-05)
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solow-decomposition

Matrix-decomposition estimators complementing [solow-multivariate]'s classical PCA / factor / rotation surface.

[KernelPca] — Schölkopf-Smola-Müller (1998) kernel PCA with Linear, Rbf, and Polynomial kernels; centering in feature space matches sklearn's KernelPCA(fit_inverse_transform=False).
[FastIca] — Hyvärinen (1999) FastICA with the logcosh and exp nonlinearities and symmetric decorrelation.
[Nmf] — Lee-Seung (2001) multiplicative-update non-negative matrix factorisation for the Frobenius objective.

All three consume a dense n × d matrix and produce an n × k projection (KernelPCA, FastICA) or a n × k × k × d factorisation (NMF). Deterministic under a caller-supplied seed.