Worst-case Recovery Guarantees for Least Squares Approximation Using Random Samples
Worst-case Recovery Guarantees for Least Squares Approximation Using Random Samples
Abstract We construct a least squares approximation method for the recovery of complex-valued functions from a reproducing kernel Hilbert space on $$D \subset \mathbb {R}^d$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>D</mml:mi> <mml:mo>⊂</mml:mo> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msup> </mml:mrow> </mml:math> . The nodes are drawn at random for the whole class of …