TY - GEN

T1 - Chromatic nearest neighbor searching: A query sensitive approach

AU - Mount, David M

AU - Netanyahu, N.

AU - Silverman, Ruth

AU - Wu, Angela Y.

N1 - Place of conference:Quebec City, Quebec, Canada

PY - 1995

Y1 - 1995

N2 - The nearest neighbor problem is that of preprocessing a set P of n data
points in Rd so that, given any query point q, the closest point in P to q can be
determined efficiently. In the chromatic nearest neighbor problem, each point
of P is assigned a color, and the problem is to determine the color of the nearest
point to the query point. More generally, given k ≥ 1, the problem is to
determine the color occurring most frequently among the k nearest neighbors.
The chromatic version of the nearest neighbor problem is used in many applications
in pattern recognition and learning. In this paper we present a simple
algorithm for solving the chromatic k nearest neighbor problem. We provide
a query sensitive analysis, which shows that if the color classes form spatially
well separated clusters (as often happens in practice), then queries can be answered
quite efficiently. We also allow the user to specify an error bound ε ≥ 0, and consider the same problem in the context of approximate nearest neighbor
searching. We present empirical evidence that for well clustered data sets, this
approach leads to significant improvements in efficiency.
Key words: Chromatic nearest neighbors, classification algorithms, pattern
recognition, multidimensional searching, BBD trees, branch and bound search,
query sensitive analysis.

AB - The nearest neighbor problem is that of preprocessing a set P of n data
points in Rd so that, given any query point q, the closest point in P to q can be
determined efficiently. In the chromatic nearest neighbor problem, each point
of P is assigned a color, and the problem is to determine the color of the nearest
point to the query point. More generally, given k ≥ 1, the problem is to
determine the color occurring most frequently among the k nearest neighbors.
The chromatic version of the nearest neighbor problem is used in many applications
in pattern recognition and learning. In this paper we present a simple
algorithm for solving the chromatic k nearest neighbor problem. We provide
a query sensitive analysis, which shows that if the color classes form spatially
well separated clusters (as often happens in practice), then queries can be answered
quite efficiently. We also allow the user to specify an error bound ε ≥ 0, and consider the same problem in the context of approximate nearest neighbor
searching. We present empirical evidence that for well clustered data sets, this
approach leads to significant improvements in efficiency.
Key words: Chromatic nearest neighbors, classification algorithms, pattern
recognition, multidimensional searching, BBD trees, branch and bound search,
query sensitive analysis.

UR - https://scholar.google.co.il/scholar?q=Chromatic+Nearest+Neighbor+Searching%3A+A+Query+Sensitive+Approach&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - Seventh Canadian Conference on Computational Geometry

ER -