Conventionally, the k nearest-neighbor (kNN) classification is implemented with use of Euclidean distance-based measures, which are mainly one-to-one similarity relationships such as to lose connections between different samples. As a strategy alleviate this issue, coefficients coded by sparse representation have played role gauger for well. Although SR enjoy remarkable discrimination nature on...

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We obtain new asymptotic results about systems of N particles governed by Riesz interactions involving k-nearest neighbors each particle as $$N\rightarrow \infty $$ . These include a generalization to weighted potentials with external field. Such offer an appealing alternative other approaches for reducing the computational complexity N-body interaction. find first-order term large asymptotics ...

DEFINITION Given a set of n points and a query point, q, the nearest-neighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a two-dimensional space with a query point, q. The right shows the problem solution, s. Figure 1: An example of a nearest-neighbor problem dom...