SemiSupervised.LabelSpreading

interface LabelSpreadingOptions {
    kernel?: 'rbf' | 'knn' | ((X: number[][], Y: number[][]) => number[][]);
    gamma?: number;
    nNeighbors?: number;
    alpha?: number;
    maxIter?: number;
    tol?: number;
}
constructor(options: LabelSpreadingOptions = {})

Label spreading assigns labels to unlabeled data using a normalized graph and soft clamping controlled by alpha.

Methods

• fit(trainX: number[][], trainY: number[]): void
• predict(testX: number[][]): number[]
• predictProba(testX: number[][]): number[][]

Example

const ls = new LabelSpreading();
ls.fit(trainX, trainY);
const preds = ls.predict(testX);

Practical guide: LabelSpreading in JavaScript and TypeScript

LabelSpreading performs smoother semi-supervised label diffusion with regularization to reduce over-confident propagation.

When to use LabelSpreading

• You want semi-supervised learning with stronger stability than pure propagation.
• Graph neighborhoods are useful but somewhat noisy.
• You need better robustness for low-label datasets.

Implementation workflow

1. Create labeled/unlabeled split and construct feature graph inputs.
2. Fit LabelSpreading and monitor convergence behavior.
3. Tune kernel and regularization parameters with validation labels.

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

Search intents this page targets

• LabelSpreading JavaScript
• LabelSpreading TypeScript
• LabelSpreading browser machine learning
• @kanaries/ml LabelSpreading