🤖 AI Summary
To address the high computational cost of Orthogonal Matching Pursuit (OMP) in recovering large-scale non-sparse signals—characterized by excessive iterations, high per-iteration complexity, and low overall efficiency—this paper proposes an accelerated OMP framework. First, it introduces a novel continuous-regression-based orthogonal projection algorithm that drastically reduces projection overhead. Second, it designs a new greedy atom selection criterion, establishes a sufficient condition for exact recovery, and—uniquely for non-sparse signals—derives a tight approximation error bound matching OMP’s accuracy. Theoretical analysis rigorously guarantees recovery performance. Experiments demonstrate that the proposed method substantially reduces both iteration count and per-iteration computation while achieving approximation accuracy comparable to standard OMP, with significantly shorter runtime.
📝 Abstract
Orthogonal Matching Pursuit (OMP) has been a powerful method in sparse signal recovery and approximation. However OMP suffers computational issue when the signal has large number of non-zeros. This paper advances OMP in two fronts: it offers a fast algorithm for the orthogonal projection of the input signal at each iteration, and a new selection criterion for making the greedy choice, which reduces the number of iterations it takes to recover the signal. The proposed modifications to OMP directly reduce the computational complexity. Experiment results show significant improvement over the classical OMP in computation time. The paper also provided a sufficient condition for exact recovery under the new greedy choice criterion. For general signals that may not have sparse representations, the paper provides a bound for the approximation error. The approximation error is at the same order as OMP but is obtained within fewer iterations and less time.