334 search results - page 2 / 67 » Local Dimensionality Reduction: A New Approach to Indexing H... |
KDD
2001
ACM
14 years 4 months ago
2001
ACM
The problem of similarity search (query-by-content) has attracted much research interest. It is a difficult problem because of the inherently high dimensionality of the data. The ...
ICDM
2002
IEEE
13 years 9 months ago
2002
IEEE
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were pro...
PODS
2000
ACM
13 years 8 months ago
2000
ACM
In this paper, we propose a new tunable index scheme, called iMinMax , that maps points in high dimensional spaces to single dimension values determined by their maximum or minimu...
DASFAA
2005
IEEE
13 years 10 months ago
2005
IEEE
Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering “curse...
ICDE
2003
IEEE
14 years 5 months ago
2003
IEEE
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well known approach to o...