Decomposition.SparsePCA

Sparse Principal Components Analysis using truncated power iteration with soft thresholding. The algorithm stops when the updates change by less than tol or when maxIter is reached.

Algorithm

Each component is extracted by iterative thresholding of the covariance matrix. The process encourages sparsity by shrinking small coefficients towards zero.

interface SparsePCAProps {
    nComponents?: number | null;
    alpha?: number;
    maxIter?: number;
    tol?: number;
}
constructor(props: SparsePCAProps = {})

Parameters

• nComponents (number | null, default null): number of sparse components to compute. null keeps all components.
• alpha (number, default 1): sparsity controlling parameter. Higher values lead to more zero coefficients.
• maxIter (number, default 100): maximum number of iterations for each component.
• tol (number, default 1e-8): stopping criterion for convergence of the iterative updates.

Example

const transformer = new SparsePCA({ nComponents: 5, alpha: 0.1 });
transformer.fit(X);
const T = transformer.transform(X_test);

Practical guide: SparsePCA in JavaScript and TypeScript

SparsePCA learns sparse components so each latent dimension uses only a subset of original features.

When to use SparsePCA

• Interpretability of component-feature relationships is important.
• You need dimensionality reduction with built-in sparsity constraints.
• Dense PCA components are too hard to explain to stakeholders.

Implementation workflow

1. Scale input features and choose sparsity-related hyperparameters.
2. Fit SparsePCA and inspect component loadings for feature selection signals.
3. Use sparse transformed outputs in linear or tree-based downstream models.

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

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