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• W2111271723 abstract "In this paper we provide a new analysis of compressive least squares regression that removes a spurious log N factor from previous bounds, where N is the number of training points. Our new bound has a clear interpretation and reveals meaningful structural properties of the linear regression problem that makes it solvable effectively in a small dimensional random subspace. In addition, the main part of our analysis does not require the compressive matrix to have the JohnsonLindenstrauss property, or the RIP property. Instead, we only require its entries to be drawn i.i.d. from a 0-mean symmetric distribution with finite first four moments." @default.
• W2111271723 created "2016-06-24" @default.
• W2111271723 creator A5001135130 @default.
• W2111271723 date "2014-04-02" @default.
• W2111271723 modified "2023-09-23" @default.
• W2111271723 title "New Bounds on Compressive Linear Least Squares Regression" @default.
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