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posted by martyb on Wednesday August 15 2018, @01:02PM   Printer-friendly
from the won't-you-be-my-neighbor? dept.

The nearest neighbor problem asks where a new point fits in to an existing data set. A few researchers set out to prove that there was no universal way to solve it. Instead, they found such a way.

If you were opening a coffee shop, there's a question you'd want answered: Where's the next closest cafe? This information would help you understand your competition.

This scenario is an example of a type of problem widely studied in computer science called "nearest neighbor" search. It asks, given a data set and a new data point, which point in your existing data is closest to your new point? It's a question that comes up in many everyday situations in areas such as genomics research, image searches and Spotify recommendations.

And unlike the coffee shop example, nearest neighbor questions are often very hard to answer. Over the past few decades, top minds in computer science have applied themselves to finding a better way to solve the problem. In particular, they've tried to address complications that arise because different data sets can use very different definitions of what it means for two points to be "close" to one another.

Now, a team of computer scientists has come up with a radically new way of solving nearest neighbor problems. In a pair of papers, five computer scientists have elaborated the first general-purpose method of solving nearest neighbor questions for complex data.

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  • (Score: 5, Informative) by c0lo on Wednesday August 15 2018, @01:12PM (1 child)

    by c0lo (156) on Wednesday August 15 2018, @01:12PM (#721757) Journal
    Starting Score:    1  point
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    Total Score:   5  
  • (Score: 1) by khallow on Sunday August 19 2018, @01:35AM

    by khallow (3766) Subscriber Badge on Sunday August 19 2018, @01:35AM (#723234) Journal
    There's some serious machinery hiding in those papers. What's interesting about this is that the machinery appears new. For example, I've half-heartedly traced back the use of "non-linear spectral gaps" and "expanders" to a 2007 paper [] by Vincent Lafforgue (which is at present impenetrable to me since my command of French is, shall we say, very distant from perfect?). To move from an abstract paper in normed Banach spaced to an application in computer science (even if it's a bit on the theoretical side) in around 11 years is a pretty impressive display of cross-disciplinary activity.