The convex polyhedra technique: an index structure for high-dimensional space
Jiyuan An, Hanxiong Chen, Kazutaka Furuse, Masahiro Ishikawa, Nobuo Ohbo · 2002
This paper proposes a new dimensionality reduction technique and an indexing mechanism for high dimensional data sets in which data points are not uniformly distributed. The proposed technique decomposes a data space into convex polyhedra, and the dimensionality of each data point is reduced according to which polyhedron includes the data point. One of the advantages of the proposed technique is that it reduces the dimensionality locally. This local dimensionality reduction contributes to improve indexing mechanisms for non-uniformly distributed data sets.