uve_select — UVE — Uninformative Variable Elimination

Group: Variable selector · Registry tolerance: 1e-06

Description

UVE noise-thresholded selection (§18 Phase 5d)

Registry note — R plsVarSel::mcuve_pls Centner et al. 1996 Monte-Carlo UVE — augment X with ncol(X) uniform-noise columns, threshold real |mean/sd| stability scores against the noise max (fallback to top-ncomp |RI| when the survivor set is too small). Default _uve_select_pls4all path mirrors the same R call with seed=11, giving bit-exact mask parity. The C++ fixed-noise-count kernel is opt-in via legacy=True.

Parameters

Name	Type	Default	Notes
n_components	int	2	current public binding signature
noise_features	int	50	current public binding signature
noise_seed	int	0	current public binding signature
min_features	int | None	None	current public binding signature

Explanations

Bibliographic source

Centner, V., Massart, D. L., de Noord, O. E., de Jong, S., Vandeginste, B. M. & Sterna, C. (1996). Elimination of uninformative variables for multivariate calibration. Analytical Chemistry 68(21), 3851–3858. Verified primary link: https://doi.org/10.1021/ac960321m.

Mathematical principle

UVE introduces a clever threshold: augment \(\mathbf{X}\) with \(p\) columns of deterministic artificial noise (unit-variance Gaussian, fixed seed), fit a PLS on the augmented \([\mathbf{X}, \mathbf{X}_{\mathrm{noise}}]\), then compute MC-UVE stability ratios for all columns. The noise columns establish a baseline distribution of stabilities under the null hypothesis ‘this column contributes nothing’. Any real feature whose stability falls below the maximum stability of the noise columns is eliminated.

The threshold is therefore data-adaptive — it tightens automatically as \(n\) grows (noise stabilities concentrate) and relaxes when the SNR is low. UVE is the canonical starting point for any chemometrics paper proposing a new selector; everything since is benchmarked against it.

Appropriate uses

Eliminating variables whose PLS coefficient stability is no better than injected noise variables.

Limits and validation

Results depend on resampling, noise construction, and scaling; UVE must be rerun inside each validation fold.

Implementation

n4m_feature_selection_uve_select. Reference: R plsVarSel.

Sources and provenance

Current implementation: cpp/src/core/uve_selection.cpp.

API and bindings

C ABI (ABI 2): n4m_feature_selection_uve_select. Use the linked public header for the exact signature, configuration, and result handles.

Python (verified public re-export):

from n4m.feature_selection.wrapper import UVE

Source signature: UVE(*, n_components: int = 2, noise_features: int = 50, noise_seed: int = 0, min_features: int | None = None) (n4m/_impl/selection.py).

R (source-verified): uve_select(X, Y, n_components, noise_features = NULL, noise_seed = 0L).

The source signature has additional required inputs, so no example call is fabricated.

MATLAB / Octave (source-verified): uve_select(X, Y, n_components, noise_features, noise_seed).

The source signature has additional required inputs, so no example call is fabricated.

Registry parity references 📐

• 📐 ref.r_plsvarsel (R · r) — plsVarSel 0.10.0 · strict (rmse_rel ≤ 1e-06) — R plsVarSel::mcuve_pls with a noise threshold — UVE elimination of low-stability features.

Benchmarks

Archived measurement identity. Backend labels in this table are the raw IDs recorded when the benchmark ran (including historical pls4all.* IDs). They preserve measurement provenance and do not describe a current public Python, R, or MATLAB binding; use the source-verified API and bindings section above for current entry points.

Adaptive wall-clock per cell measured against full_matrix.csv. Only backends that implement this method are listed; libraries without the method are omitted.

Verdict  ·  ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance  ·  ✓ bind = archived binding-harness result agrees with the C++ baseline  ·  ⇄ cross-check = documented by-design selector/RNG/model, noncanonical API/facade convention, or secondary oracle  ·  ✗ divergent  ·  ⚠ error  ·  — not run. The fastest backend per column is marked 🏆.

Reference gate: strict — numeric equivalence (rmse_rel_tol ≤ 1e-06).

Rows tagged with 📐 are the canonical parity references for this method (declared in parity_timing.registry). C++ and external rows show reference parity; archived language-harness rows show binding parity against the C++ backend. Hover the icon for role and tolerance band.

1 thread

Backend	Parity	200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp	✓ J 1.00	1.5 s
Python · archived pls4all benchmark
pls4all.python	✓ J 1.00	1.7 s
pls4all.sklearn	⇄ J 0.75	3.28 ms🏆
R · archived pls4all benchmark
pls4all.R	⇄ J 0.75	10.3 ms
pls4all.R.formula	⇄ J 0.75	7.89 ms
pls4all.R.mdatools	⇄ J 0.75	6.20 ms
pls4all.R.pls	⇄ J 0.75	6.77 ms
R · external
📐ref.r_plsvarsel	source	222.0 ms

3 threads

Backend	Parity	200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp	✓ J 1.00	607.0 ms
Python · archived pls4all benchmark
pls4all.python	✓ J 1.00	640.4 ms
pls4all.sklearn	⇄ J 0.75	1.81 ms🏆
R · archived pls4all benchmark
pls4all.R	⇄ J 0.75	4.61 ms
pls4all.R.formula	⇄ J 0.75	5.51 ms
pls4all.R.mdatools	⇄ J 0.75	5.45 ms
pls4all.R.pls	⇄ J 0.75	5.70 ms
R · external
📐ref.r_plsvarsel	source	397.3 ms

10 threads

Backend	Parity	200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp	✓ J 1.00	737.2 ms
Python · archived pls4all benchmark
pls4all.python	✓ J 1.00	1.5 s
pls4all.sklearn	⇄ J 0.75	2.82 ms🏆
R · archived pls4all benchmark
pls4all.R	⇄ J 0.75	9.76 ms
pls4all.R.formula	⇄ J 0.75	9.70 ms
pls4all.R.mdatools	⇄ J 0.75	5.84 ms
pls4all.R.pls	⇄ J 0.75	6.21 ms
R · external
📐ref.r_plsvarsel	source	230.2 ms

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See also: benchmark overview · methods index · interactive dashboard