Learning Polynomials of Few Relevant Dimensions

COLT, pp. 1161-1227, 2020.

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We introduce a new filtered PCA approach to get a warm start for the true subspace and use geodesic SGD to boost to arbitrary accuracy; our techniques may be of independent interest, especially for problems dealing with subspace recovery or analyzing SGD on manifolds

Abstract:

Polynomial regression is a basic primitive in learning and statistics. In its most basic form the goal is to fit a degree $d$ polynomial to a response variable $y$ in terms of an $n$-dimensional input vector $x$. This is extremely well-studied with many applications and has sample and runtime complexity $\Theta(n^d)$. Can one achieve be...More

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Introduction
  • Consider the classical polynomial regression problem in learning and statistics. In its most basic form, the authors receive samples of the form (x, y) with x ∈ Rn coming from some distribution and y is P (x) for a degree at most d polynomial in x.
  • Given samples (x, y = P (x)) where x ∼ N (0, Idn), and P is an unknown degreed, rank-r polynomial, can one approximately recover the subspace defining P efficiently?
Highlights
  • Consider the classical polynomial regression problem in learning and statistics
  • We introduce a new filtered PCA approach to get a warm start for the true subspace and use geodesic SGD to boost to arbitrary accuracy; our techniques may be of independent interest, especially for problems dealing with subspace recovery or analyzing SGD on manifolds
  • We argue that the top eigenvector of the above matrix will have most of its mass in U ∗ and this gives us our vector vl+1
  • We argue that with high probability, the dominant term given by Y is large and the error from Taylor approximation is small
  • While we have already seen that Lemma 7.2 is needed to prove Theorem 7.1, Lemmas 7.6 and 7.7 will be crucial to our arguments in later sections, where we argue that at each step t we make progress scaling with the distance dP (V (t), V ∗) and need that this distance is comparable to the initial distance dP (V (0), V ∗)
  • We argue that with high probability, the dominant term given by X is large, while the error terms from Taylor approximation and from the trigonometric corrections are small
Results
  • For all δ > 0 and ǫ ∈ (0, 1), there is an efficient algorithm that takes N = C0(r, d, α)(ln(n/δ))c0d · n log2(1/ǫ) samples (x, P (x)), where x ∼ N (0, Idn) and P is an unknown α-non-degenerate rank r, degree-d polynomial defined by hidden subspace U ∗, and outputs
  • For all δ > 0 and ǫ ∈ (0, 1), there is an efficient algorithm that takes N = C(r, d, α)n log(1/δ)/ǫ2 samples (x, P (x)) for x ∼ N (0, Idn) and unknown P which is α-non-degenerate of rank r, and outputs a subspace U such that with probability at least 1− δ, dP (U, U ∗) < ǫ.
  • Let Θ∗ = (c∗, v∗) be one of the two possible realizations of D for which v∗ ∈ Sn−1, and suppose the authors already have a warm start of Θ = (c, v), where the coefficients c and c∗ define the univariate degree-d polynomials p(z)
  • The workaround for the issue posed in Section 2.2.1 is clear at least in the rank-1 case: to avoid moving in the wasteful directions which are orthogonal to the current iterate v, compute the vanilla gradient and project to the orthogonal complement of v.
  • The authors will let Pnνc,ron,dd denote the set of all νcond non-degenerate rank r polynomials P of degree at most d in n variables that satisfy the normalization conditions Prν,cdond for Prν,cro,ndd .
  • The following says that if a set of r orthogonal unit vectors all have large component in U ∗, their span is close to the true subspace in the sense of either of the distances above.
  • Let D denote the distribution (X, Y ) where Y = P (X) is a α non-degerate polynomial of rank r and degree at most d as in the hypothesis of the theorem.
Conclusion
  • Note that Lemma 4.2 already gives a nontrivial algorithmic guarantee for l = 0: given exact access to Mτ∅, the authors can recover a vector inside the true subspace by taking its top eigenvector.
  • For c = {cI } ∈ RM , where the author ranges over multisets of size at most d consisting of elements of [r], and V ∈ Stnr , let parameters Θ = (c, V ) correspond to a rank-r polynomial
Summary
  • Consider the classical polynomial regression problem in learning and statistics. In its most basic form, the authors receive samples of the form (x, y) with x ∈ Rn coming from some distribution and y is P (x) for a degree at most d polynomial in x.
  • Given samples (x, y = P (x)) where x ∼ N (0, Idn), and P is an unknown degreed, rank-r polynomial, can one approximately recover the subspace defining P efficiently?
  • For all δ > 0 and ǫ ∈ (0, 1), there is an efficient algorithm that takes N = C0(r, d, α)(ln(n/δ))c0d · n log2(1/ǫ) samples (x, P (x)), where x ∼ N (0, Idn) and P is an unknown α-non-degenerate rank r, degree-d polynomial defined by hidden subspace U ∗, and outputs
  • For all δ > 0 and ǫ ∈ (0, 1), there is an efficient algorithm that takes N = C(r, d, α)n log(1/δ)/ǫ2 samples (x, P (x)) for x ∼ N (0, Idn) and unknown P which is α-non-degenerate of rank r, and outputs a subspace U such that with probability at least 1− δ, dP (U, U ∗) < ǫ.
  • Let Θ∗ = (c∗, v∗) be one of the two possible realizations of D for which v∗ ∈ Sn−1, and suppose the authors already have a warm start of Θ = (c, v), where the coefficients c and c∗ define the univariate degree-d polynomials p(z)
  • The workaround for the issue posed in Section 2.2.1 is clear at least in the rank-1 case: to avoid moving in the wasteful directions which are orthogonal to the current iterate v, compute the vanilla gradient and project to the orthogonal complement of v.
  • The authors will let Pnνc,ron,dd denote the set of all νcond non-degenerate rank r polynomials P of degree at most d in n variables that satisfy the normalization conditions Prν,cdond for Prν,cro,ndd .
  • The following says that if a set of r orthogonal unit vectors all have large component in U ∗, their span is close to the true subspace in the sense of either of the distances above.
  • Let D denote the distribution (X, Y ) where Y = P (X) is a α non-degerate polynomial of rank r and degree at most d as in the hypothesis of the theorem.
  • Note that Lemma 4.2 already gives a nontrivial algorithmic guarantee for l = 0: given exact access to Mτ∅, the authors can recover a vector inside the true subspace by taking its top eigenvector.
  • For c = {cI } ∈ RM , where the author ranges over multisets of size at most d consisting of elements of [r], and V ∈ Stnr , let parameters Θ = (c, V ) correspond to a rank-r polynomial
Related work
  • Filtering Data by Thresholding Our algorithm for obtaining a warm start (see Theorem 2.1) relies on filtering the data via some form of thresholding. This general paradigm has been used in other, unrelated contexts like robustness, see [SS19, SS18, DKK+19a, Li18b, DKK+19b, DKK+17] and the references therein, though typically the points which are smaller than some threshold are removed, whereas our algorithm, TrimmedPCA, is an intriguing case where the opposite kind of filter is applied.

    Riemannian Optimization It is beyond the scope of this paper to reliably survey the vast literature on Riemannian optimization methods, and we refer the reader to the standard references on the subject [Udr[94], AMS09] which mostly provide asymptotic convergence guarantees, as well as the thesis of Boumal [Bou14] and the references therein. Some notable lines of work include optimization with respect to orthogonality constraints [EAS98], applications to low-rank matrix and tensor completion [MMBS13, Van[13], IAVHDL11, KSV14], dictionary learning [SQW16], independent component analysis [SJG09], canonical correlation analysis [LWW15], matrix equation solving [VV10], complexity theory and operator scaling [AZGL+18], subspace tracking [BNR10, ZB], and building a theory of geodesically convex optimization [ZS16, HS15, ZRS16].

    We remark that the update rule we use in our boosting algorithm is very similar to that of [BNR10, ZB], as their and our work are based on geodesics on the Grassmannian manifold. That said, they solve a very different problem from ours, and the analysis is quite different.
Funding
  • ∗This work was supported in part by a Paul and Daisy Soros Fellowship, NSF CAREER Award CCF-1453261, and NSF Large CCF-1565235
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