# `uve_select` — UVE — Uninformative Variable Elimination
_Group_: **Variable selector** · _Registry tolerance_: `1e-06`
## Description
UVE noise-thresholded selection (§18 Phase 5d)
From the `pls4all.sklearn.UVESelector` docstring:
> Uninformative Variable Elimination (Centner 1996) with artificial
noise variables for the threshold.
> **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` | Number of latent components extracted (k). |
| `noise_features` | `int` | `50` | Number of artificial noise variables appended to X for the UVE threshold. |
| `noise_seed` | `int` | `0` | Seed for the appended noise variables. |
| `min_features` | `int | None` | `None` | Minimum number of variables the selector is allowed to keep (defaults to `n_components`). |
## 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.
### 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.
### Implementation
`n4m_uve_select`. Reference: R `plsVarSel`.
MATLAB header (`bindings/matlab/+pls4all/uve_select.m`):
```text
pls4all.uve_select Uninformative Variable Elimination (artificial-noise threshold).
```
### Usage
Every pls4all binding tab dispatches into the same C kernel; the external libraries listed at the bottom of the page are the parity references registered in `benchmarks.parity_timing.registry`. Switch tabs to read the same fit in your language. The R package now ships drop-in-compatible facades for the CRAN `pls` package (`plsr`, `pcr`, `mvr`) and for the `mdatools::pls(x, y, ...)` matrix idiom — those tabs appear only on the methods that have a meaningful equivalence.
**pls4all bindings**
::::{tab-set}
:class: pls4all-bindings
:::{tab-item} C ABI · libn4m
:sync: c
:class-label: lang-c
```c
/* C ABI — libn4m */
n4m_context_t* ctx = n4m_context_create();
n4m_config_t* cfg = n4m_config_create();
n4m_method_result_t* res = NULL;
n4m_uve_select_fit(ctx, cfg, &x_view, &y_view, /* hyperparams */, &res);
/* … read coefficients / mask / scores via */
/* n4m_method_result_get_double_matrix / vector / scalar … */
n4m_method_result_destroy(res);
n4m_config_destroy(cfg);
n4m_context_destroy(ctx);
```
:::
:::{tab-item} Python · pls4all (raw)
:sync: python-raw
:class-label: lang-python
```python
import pls4all
from pls4all._methods import uve_select_fit
with pls4all.Context() as ctx, pls4all.Config() as cfg:
res = uve_select_fit(ctx, cfg, X, y, n_components=4)
# then: res.matrix("predictions"), res.matrix("coefficients"),
# res.vector("mask"), res.scalar("intercept"), …
```
:::
:::{tab-item} Python · pls4all.sklearn
:sync: python-sklearn
:class-label: lang-python
```python
from pls4all.sklearn import UVESelector
mdl = UVESelector(n_components=2, noise_features=50, noise_seed=0, min_features=None)
mdl.fit(X, y)
y_hat = mdl.predict(X_test)
```
:::
:::{tab-item} R · pls4all_method()
:sync: r-dispatcher
:class-label: lang-r
```r
library(pls4all)
# Unified low-level dispatcher (May 2026 R cleanup):
res <- pls4all_method("uve_select", X, y,
n_components = 4L, params = list(noise_features = 5L, noise_seed = 11L))
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.
```
:::
:::{tab-item} MATLAB · pls4all (MEX)
:sync: matlab-mex
:class-label: lang-matlab
```matlab
res = pls4all.uve_select(X, y, 4);
% see header of bindings/matlab/+pls4all/uve_select.m for full
% parameter surface:
% res = uve_select(X, Y, n_components, noise_features, noise_seed)
yhat = predict(res, Xtest);
```
:::
:::{tab-item} MATLAB · pls4all (classdef)
:sync: matlab-classdef
:class-label: lang-matlab
_No idiomatic classdef wrapper — invoke `pls4all.fit("uve_select", X, y, …)` directly from the unified MEX factory._
:::
::::
**Registry parity references** 📐
:::{card}
:class-card: external-refs
- 📐 **`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
Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). 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 = pls4all binding agrees with the C++ baseline · ✗ 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`](../benchmarks/methodology.md)). C++ and external rows show reference parity; pls4all language bindings show binding parity against the C++ backend. Hover the icon for role and tolerance band.
::::{tab-set}
:class: parity-tabs
:::{tab-item} 1 thread
:sync: threads-1
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ≈ | 523.5 ms | 2.4 s | 4.4 s | 20.4 s | 716.2 ms | 761.8 ms | 6.1 s🏆 | 75.2 s🏆 | 388.7 s | 172.8 s🏆 | — | — | — | — |
pls4all.cpp.blas+omp | ≈ | 496.0 ms | 2.1 s🏆 | 4.4 s🏆 | 19.6 s | 712.5 ms | 887.2 ms | 6.1 s | 77.4 s | 396.9 s | 189.3 s | — | — | — | — |
pls4all.cpp.omp | ≈ | 495.3 ms | 2.4 s | 4.7 s | 18.1 s🏆 | 719.2 ms | 846.0 ms | 6.5 s | 79.7 s | 426.5 s | 193.5 s | — | — | — | — |
pls4all.cpp.ref | ≈ | 511.9 ms | 2.3 s | 4.6 s | 20.9 s | 732.2 ms | 756.7 ms | 6.3 s | 76.0 s | 383.1 s🏆 | 180.2 s | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | 522.5 ms | — | — | — | 708.2 ms | 757.5 ms | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✗ +1e+00 | 2.91 ms | — | — | — | 2.83 ms | 2.86 ms | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | 10.5 ms | — | — | — | 6.85 ms | 11.7 ms | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | 19.7 ms | — | — | — | 8.22 ms | 10.6 ms | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | 19.6 ms | — | — | — | 6.98 ms | 9.94 ms | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | 19.7 ms | — | — | — | 7.83 ms | 10.4 ms | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | 4.16 ms | — | — | — | 2.69 ms | 4.44 ms | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | 5.25 ms | — | — | — | 3.27 ms | 5.11 ms | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | 73.5 ms🏆 | — | — | — | 225.9 ms🏆 | 351.7 ms🏆 | — | — | — | — | — | — | — | — |
:::
:::{tab-item} 3 threads
:sync: threads-3
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ✓ ref | — | — | — | — | 639.4 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.blas+omp | ✓ ref | — | — | — | — | 650.1 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.omp | ✓ ref | — | — | — | — | 655.3 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.ref | ✓ ref | — | — | — | — | 639.0 ms | — | — | — | — | — | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | — | — | — | — | 649.4 ms | — | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✗ +1e+00 | — | — | — | — | 1.93 ms | — | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | — | — | — | — | 5.40 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | — | — | — | — | 6.56 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | — | — | — | — | 6.49 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | — | — | — | — | 6.20 ms | — | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | — | — | — | — | 2.63 ms | — | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | — | — | — | — | 3.95 ms | — | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | — | — | — | — | 199.6 ms🏆 | — | — | — | — | — | — | — | — | — |
:::
:::{tab-item} 10 threads
:sync: threads-10
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ✓ ref | — | — | — | — | 556.7 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.blas+omp | ✓ ref | — | — | — | — | 551.2 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.omp | ✓ ref | — | — | — | — | 546.5 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.ref | ✓ ref | — | — | — | — | 555.2 ms | — | — | — | — | — | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | — | — | — | — | 541.9 ms | — | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✗ +1e+00 | — | — | — | — | 1.69 ms | — | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | — | — | — | — | 3.89 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | — | — | — | — | 4.85 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | — | — | — | — | 4.85 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | — | — | — | — | 4.71 ms | — | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | — | — | — | — | 2.37 ms | — | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | — | — | — | — | 2.70 ms | — | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | — | — | — | — | 202.7 ms🏆 | — | — | — | — | — | — | — | — | — |
:::
::::
---
_See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)