Non-Sparse PCA in High Dimensions via Cone Projected Power Iteration
This work addresses efficient PCA in high-dimensional settings for applications where eigenvectors have cone constraints, but it is incremental as it builds on power iteration with cone projections.
The paper tackles the problem of recovering the first principal eigenvector from a noisy matrix when it lies in a convex cone, proposing a cone projected power iteration algorithm that achieves polynomial time complexity and small error under certain conditions, with numerical experiments showing shorter run time and smaller error compared to existing methods.
In this paper, we propose a cone projected power iteration algorithm to recover the first principal eigenvector from a noisy positive semidefinite matrix. When the true principal eigenvector is assumed to belong to a convex cone, the proposed algorithm is fast and has a tractable error. Specifically, the method achieves polynomial time complexity for certain convex cones equipped with fast projection such as the monotone cone. It attains a small error when the noisy matrix has a small cone-restricted operator norm. We supplement the above results with a minimax lower bound of the error under the spiked covariance model. Our numerical experiments on simulated and real data, show that our method achieves shorter run time and smaller error in comparison to the ordinary power iteration and some sparse principal component analysis algorithms if the principal eigenvector is in a convex cone.