The Sparse Principal Component of a Constant-rank Matrix
This provides a theoretical breakthrough for sparse PCA in specific matrix classes, enabling efficient computation in applications like data analysis and machine learning where such matrices arise.
The paper tackles the NP-hard problem of computing the sparse principal component of a matrix by proving it becomes polynomially computable for positive semidefinite matrices with constant rank, and designs an algorithm with complexity O(N^{D+1}) that is parallelizable and memory efficient.
The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem ${\mathcal{NP}}$-hard. In this work, we prove that, if the matrix is positive semidefinite and its rank is constant, then its sparse principal component is polynomially computable. Our proof utilizes the auxiliary unit vector technique that has been recently developed to identify problems that are polynomially solvable. Moreover, we use this technique to design an algorithm which, for any sparsity value, computes the sparse principal component with complexity ${\mathcal O}\left(N^{D+1}\right)$, where $N$ and $D$ are the matrix size and rank, respectively. Our algorithm is fully parallelizable and memory efficient.