Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing.  establishes a lor bound on the mean-squared error, which holds regardless of the sensing/design matrix being used and regardless of the estimation procedure. This lor bound very nearly matches the known upper bound one gets by taking a random projection of the sparse vector follod by an l1 estimation procedure such as the Dantzig selector. In this sense, compressive sensing techniques cannot essentially be improved.
['Emmanuel J. Candès', 'Mark A. Davenport']
I am writing a research paper on a idea How well can we estimate a sparse vector
[{"from": "human", "value": "I am writing a research paper on a idea How well can we estimate a sparse vector"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing.  establishes a lor bound on the mean-squared error, which holds regardless of the sensing/design matrix being used and regardless of the estimation procedure. This lor bound very nearly matches the known upper bound one gets by taking a random projection of the sparse vector follod by an l1 estimation procedure such as the Dantzig selector. In this sense, compressive sensing techniques cannot essentially be improved."}]