Analysis of Truncated Orthogonal Iteration for Sparse Eigenvector Problems
This work addresses sparse eigenvector estimation problems in computational science and engineering, representing an incremental improvement with novel algorithmic variants.
The authors tackled the problem of estimating sparse eigenvectors for high-dimensional systems by proposing two variants of Truncated Orthogonal Iteration to compute multiple leading eigenvectors with sparsity constraints simultaneously. They demonstrated that these methods achieve state-of-the-art results quickly and with minimal parameter tuning across various test datasets including MNIST, sea surface temperature, and 20 newsgroups.
A wide range of problems in computational science and engineering require estimation of sparse eigenvectors for high dimensional systems. Here, we propose two variants of the Truncated Orthogonal Iteration to compute multiple leading eigenvectors with sparsity constraints simultaneously. We establish numerical convergence results for the proposed algorithms using a perturbation framework, and extend our analysis to other existing alternatives for sparse eigenvector estimation. We then apply our algorithms to solve the sparse principle component analysis problem for a wide range of test datasets, from simple simulations to real-world datasets including MNIST, sea surface temperature and 20 newsgroups. In all these cases, we show that the new methods get state of the art results quickly and with minimal parameter tuning.