کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4605005 | 1337537 | 2014 | 24 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Learning non-parametric basis independent models from point queries via low-rank methods Learning non-parametric basis independent models from point queries via low-rank methods](/preview/png/4605005.png)
We consider the problem of learning multi-ridge functions of the form f(x)=g(Ax)f(x)=g(Ax) from point evaluations of f. We assume that the function f is defined on an ℓ2ℓ2-ball in RdRd, g is twice continuously differentiable almost everywhere, and A∈Rk×dA∈Rk×d is a rank k matrix, where k≪dk≪d. We propose a randomized, polynomial-complexity sampling scheme for estimating such functions. Our theoretical developments leverage recent techniques from low rank matrix recovery, which enables us to derive a polynomial time estimator of the function f along with uniform approximation guarantees. We prove that our scheme can also be applied for learning functions of the form: f(x)=∑i=1kgi(aiTx), provided f satisfies certain smoothness conditions in a neighborhood around the origin. We also characterize the noise robustness of the scheme. Finally, we present numerical examples to illustrate the theoretical bounds in action.
Journal: Applied and Computational Harmonic Analysis - Volume 37, Issue 3, November 2014, Pages 389–412