Classification of high-dimensional data with spiked covariance matrix structure

no code implementations • 5 Oct 2021 • Yin-Jen Chen, Minh Tang

We study the classification problem for high-dimensional data with $n$ observations on $p$ features where the $p \times p$ covariance matrix $\Sigma$ exhibits a spiked eigenvalues structure and the vector $\zeta$, given by the difference between the whitened mean vectors, is sparse with sparsity at most $s$.

Classification Dimensionality Reduction +1