PCACompute

Core and matricesfunctionOpenCV 5.0.0

import { PCACompute } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Principal component analysis →

Find orthogonal directions that explain the greatest variance in centred data.

VISUAL WALKTHROUGHMachine learning

REFERENCE INPUTCorrelated features

The observations vary mainly along a diagonal direction.

→

RESULTKeep one principal coordinate

Two-dimensional observations become one scalar per sample. Variation perpendicular to the chosen axis is discarded.

Mean, covariance direction and projections are computed from the displayed samples.

wrap PCA::operator()

PCACompute(data: Mat, mean: Mat, eigenvectors: Mat, maxComponents: number): void;

2 available overloads

PCACompute(data: Mat, mean: Mat, eigenvectors: Mat): void;

PCACompute(data: Mat, mean: Mat, eigenvectors: Mat, maxComponents: number): void;

data

data argument (Mat).

mean

Input/output value, modified by the native operation. mean argument (Mat).

eigenvectors

Output destination, filled by the native operation. eigenvectors argument (Mat).

maxComponents

max components argument (number).

Upstream declaration ↗

These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.