Sp-GD recovers sparse max-affine parameters to epsilon accuracy with O(s log(d/s)) samples in the noise-free case under sub-Gaussian assumptions, supported by sparse-PCA initialization and an RMD transformation for generalized polynomials.
Let {xi}n i=1 be independent copies of a random vector x that satisfies Assumption 2.1
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Sparse Max-Affine Regression
Sp-GD recovers sparse max-affine parameters to epsilon accuracy with O(s log(d/s)) samples in the noise-free case under sub-Gaussian assumptions, supported by sparse-PCA initialization and an RMD transformation for generalized polynomials.