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Max-affine regression via first-order methods
We consider regression of a max-affine model that produces a piecewise linear model by combining affine models via the max function.
Max-Linear Regression by Convex Programming
When the $k$ linear components are equally likely to achieve the maximum, our result shows a sufficient number of noise-free observations for exact recovery scales as {$k^{4}p$} up to a logarithmic factor.
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