Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. ,  present an extension of OMP for pursuing efficiency of the index selection. Our approach, referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple (N) columns are identified per step. Using the restricted isometry property (RIP),  derive the condition for gOMP to recover the sparse signal exactly. The gOMP guarantees to reconstruct sparse signal when the sensing matrix satisfies the RIP constant . In addition,  show recovery performance and the reduced number of iteration required to recover the sparse signal.
['Seokbeop Kwon', 'Byonghyo Shim']
I am writing a research paper on a idea Generalized Orthogonal Matching Pursuit
[{"from": "human", "value": "I am writing a research paper on a idea Generalized Orthogonal Matching Pursuit"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. ,  present an extension of OMP for pursuing efficiency of the index selection. Our approach, referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple (N) columns are identified per step. Using the restricted isometry property (RIP),  derive the condition for gOMP to recover the sparse signal exactly. The gOMP guarantees to reconstruct sparse signal when the sensing matrix satisfies the RIP constant . In addition,  show recovery performance and the reduced number of iteration required to recover the sparse signal."}]