PCACompute2

Core and matricesfunctionOpenCV 5.0.0

import { PCACompute2 } 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() and add eigenvalues output parameter

PCACompute2(data: Mat, mean: Mat, eigenvectors: Mat, eigenvalues: Mat, maxComponents: number): void;

2 available overloads

PCACompute2(data: Mat, mean: Mat, eigenvectors: Mat, eigenvalues: Mat): void;

PCACompute2(data: Mat, mean: Mat, eigenvectors: Mat, eigenvalues: 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).

eigenvalues

Output destination, filled by the native operation. eigenvalues 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.