Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA).

ABSTRACT: We present a novel technique for sparse principal component analysis. This method, named Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix from the eigenvalues of the full matrix and associated sub-matrices. We explore two versions of the EESPCA method: a version that uses a fixed threshold for inducing sparsity and a version that selects the threshold via cross-validation. Relative to the state-of-the-art sparse PCA methods of Witten et al., Yuan & Zhang and Tan et al., the fixed threshold EESPCA technique offers an order-of-magnitude improvement in computational speed, does not require estimation of tuning parameters via cross-validation, and can more accurately identify tru

SUBMITTER: Frost HR 

PROVIDER: S-EPMC9187050 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature