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);