Abstract
Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of applications, including multimedia information retrieval, data mining, and pattern recognition. For such applications, the curse of high dimensionality tends to be a major obstacle in the development of efficient indexing methods. This paper addresses the problem of designing an efficient multidimensional indexing structure for high dimensional nearest neighbor search. More specifically, using self-organizing maps (SOM), high-dimensional vector data are first transformed into one-dimensional units while preserving the higher order topology by mapping similar data items to the same or the neighboring unit. Then, given a query vector, only data items whose location is close to the unit location of the query are considered as candidates. Experimental results indicate that our scheme scales well even for a very large number of dimensions.
