Technical perspective: finding a good neighbor, near and fast

Author:

Chazelle Bernard1

Affiliation:

1. Princeton University, Princeton, NJ

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.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A PROBABILISTIC ℓ1 METHOD FOR CLUSTERING HIGH-DIMENSIONAL DATA;Probability in the Engineering and Informational Sciences;2021-04-05

2. Down the Rabbit Hole: Robust Proximity Search and Density Estimation in Sublinear Space;SIAM Journal on Computing;2014-01

3. Down the Rabbit Hole: Robust Proximity Search and Density Estimation in Sublinear Space;2012 IEEE 53rd Annual Symposium on Foundations of Computer Science;2012-10

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