A Simple Algorithm for Nearest Neighbor Search in High Dimensions
نویسنده
چکیده
Finding the closest point in a high-dimensional space is a problem that often occurs in pattern recognition. Unfortunately, the complexity of most known search algorithms grows exponentially with dimension, which makes them unsuitable for high dimensions. However, for most applications, the closest point is of interest only if it is closer than some pre-defined distance. In the article ”A Simple Algorithm for Nearest Neighbor Search in High Dimensions” [1], Nene and Nayar introduce an algorithm for finding the closest point within Euclidean distance , which outperforms other popular search algorithms on a comprehensive set of benchmarks. This essay will give an overview of said article and its results.
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The problem of finding the closest point in high-dimensional spaces is common in pattern recognition. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially with dimension, making them impractical for dimensionality above 15. In nearly all applications, the closest point is of interest only if it lies within a user-specified distance e...
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