20 search results - page 4 / 4 » Approximating the distance to monotonicity in high dimension... |
COMGEO
2004
ACM
13 years 5 months ago
2004
ACM
In k-means clustering we are given a set of n data points in d-dimensional space d and an integer k, and the problem is to determine a set of k points in d , called centers, to mi...
FOCS
2004
IEEE
13 years 9 months ago
2004
IEEE
Concurrent with recent theoretical interest in the problem of metric embedding, a growing body of research in the networking community has studied the distance matrix defined by n...
ALENEX
2001
13 years 6 months ago
2001
Range searches in metric spaces can be very di cult if the space is \high dimensional", i.e. when the histogram of distances has a large mean and a small variance. The so-cal...
DEXA
2006
Springer
13 years 7 months ago
2006
Springer
Indexing high-dimensional data for efficient nearest-neighbor searches poses interesting research challenges. It is well known that when data dimension is high, the search time can...
ICCV
1999
IEEE
14 years 7 months ago
1999
IEEE
A new approach to computing a panoramic (360 degrees) depth map is presented in this paper. Our approach uses a large collection of images taken by a camera whose motion has been c...