Reconstructing curves in three (and higher) dimensional space from noisy data

  • Authors:

  • Don Coppersmith;Madhu Sudan

  • Affiliations:

  • IBM Thomas J. Watson Research Center, Yorktown Heights, New York;Massachusetts Institute of Technology, Cambridge, MA

  • Venue:
  • Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
  • Year:
  • 2003

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Abstract

We consider the task of reconstructing a curve in constant dimensional space from noisy data. We consider curves of the form C = [(x,y1,•••,yc) | yj = pj(x)], where the pj's are polynomials of low degree. Given n points in (c+1)-dimensional space, such that t of these lie on some such unknown curve C while the other n-t are chosen randomly and independently, we give an efficient algorithm to recover the curve C and the identity of the good points. The success of our algorithm depends on the relation between n, t, c and the degree of the curve C, requiring t = Ω (n deg(C)) 1/(c+1). This generalizes, in the restricted setting of random errors, the work of Sudan (J. Complexity, 1997) and of Guruswami and Sudan (IEEE Trans. Inf. Th. 1999) that considered the case c=1.