【摘 要】
：
Regression in large p,small n setting has attracted much recent attention in several fields including statistics,applied mathematics and engineering.A commo
【机 构】
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DepartmentofStatistics,TheWhartonSchoolUniversityofPennsylvania
【出 处】
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2009 International Conference on Financial Statistics and Fi
论文部分内容阅读
Regression in large p,small n setting has attracted much recent attention in several fields including statistics,applied mathematics and engineering.A commonly used framework in sparse recovery is the restricted is ometry property (RIP) which essentially requires that every subset of columns of the design matrix with certain cardinality approximately behaves like an orthonormal system.In this talk I will present a concise and unified analysis of the constrained l1 minimization method for recovery of sparse signals in three settings:noiseless,bounded error and Gaussian noise.
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