Technical perspective: finding a good neighbor, near and fast

ABSTRACT

You haven't read it yet, but you can already tell this article is going to be one long jumble of words, numbers, and punctuation marks. Indeed, but look at it differently, as a text classifier would, and you will see a single point in high dimension, with word frequencies acting as coordinates. Or take the background on your flat panel display: a million colorful pixels teaming up to make quite a striking picture. Yes, but also one single point in 106-dimensional space--that is, if you think of each pixel's RGB intensity as a separate coordinate. In fact, you don't need to look hard to find complex, heterogeneous data encoded as clouds of points in high dimension. They routinely surface in applications as diverse as medical imaging, bioinformatics, astrophysics, and finance.

REFERENCES

• 1: Alexandr Andoni , Piotr Indyk, Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions, Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science, p.459-468, October 21-24, 2006 [doi>10.1109/FOCS.2006.49]

INDEX TERMS

Primary Classification:
  E. Data
  E.2 DATA STORAGE REPRESENTATIONS

Additional Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.1 Numerical Algorithms and Problems
      F.2.2 Nonnumerical Algorithms and Problems

  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory

  H. Information Systems
  H.2 DATABASE MANAGEMENT