Results 1  10
of
3,023,752
On the Surprising Behavior of Distance Metrics in High Dimensional Space
 Lecture Notes in Computer Science
, 2001
"... In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a efficienc ..."
Abstract

Cited by 200 (2 self)
 Add to MetaCart
In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a
Nearest Neighbors In HighDimensional Spaces
, 2004
"... In this chapter we consider the following problem: given a set P of points in a highdimensional space, construct a data structure which given any query point q nds the point in P closest to q. This problem, called nearest neighbor search is of significant importance to several areas of computer sci ..."
Abstract

Cited by 93 (2 self)
 Add to MetaCart
In this chapter we consider the following problem: given a set P of points in a highdimensional space, construct a data structure which given any query point q nds the point in P closest to q. This problem, called nearest neighbor search is of significant importance to several areas of computer
EM in HighDimensional Spaces
"... Abstract—This paper considers fitting a mixture of Gaussians model to highdimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside ExpectationMaximization (EM ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Abstract—This paper considers fitting a mixture of Gaussians model to highdimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation
What is the Nearest Neighbor in High Dimensional Spaces?
, 2000
"... Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of novel database applications. As recent results show, however, the problem is a very difficult one, not only with regards to the performance issue but also to the quality ..."
Abstract

Cited by 138 (12 self)
 Add to MetaCart
Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of novel database applications. As recent results show, however, the problem is a very difficult one, not only with regards to the performance issue but also
Shape Indexing Using Approximate NearestNeighbour Search in HighDimensional Spaces
, 1997
"... Shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. When model databases are large, the use of highdimensional features is critical, due to the improved level of discrimination they can provide. Unfortunately, f ..."
Abstract

Cited by 312 (12 self)
 Add to MetaCart
Shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. When model databases are large, the use of highdimensional features is critical, due to the improved level of discrimination they can provide. Unfortunately
Finding Generalized Projected Clusters in High Dimensional Spaces
"... High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimensional data, even the concept of proximity or clustering may not be meaningful. We discuss very general techniques for projec ..."
Abstract

Cited by 195 (8 self)
 Add to MetaCart
High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimensional data, even the concept of proximity or clustering may not be meaningful. We discuss very general techniques
Chromatic Clustering in High Dimensional Space
"... Abstract. In this paper, we study a new type of clustering problem, called Chromatic Clustering, in high dimensional space. Chromatic clustering seeks to partition a set of colored points into groups (or clusters) so that no group contains points with the same color and a certain objective function ..."
Abstract
 Add to MetaCart
Abstract. In this paper, we study a new type of clustering problem, called Chromatic Clustering, in high dimensional space. Chromatic clustering seeks to partition a set of colored points into groups (or clusters) so that no group contains points with the same color and a certain objective function
Indexing Methods in HighDimensional Spaces
"... Introduction The indexing problem in highdimensional spaces in connection with image databases is an active area of research. The databases are quite large and images are usually described (abstracted) into a vector of components which are usually considered useful for recognition purposes (those ..."
Abstract
 Add to MetaCart
Introduction The indexing problem in highdimensional spaces in connection with image databases is an active area of research. The databases are quite large and images are usually described (abstracted) into a vector of components which are usually considered useful for recognition purposes (those
Resolving Bridging Descriptions in HighDimensional Space
, 1998
"... Contents 1 Introduction 1 2 Background 1 2.1 Bridging Descriptions . . . . . . . . . . . . . . . . . . . . . . . 1 2.2 The System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 HighDimensional Space . . . . . . . . . . . . . . . . . . . . . 5 2.3.1 Creating highdimensional space . ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
Contents 1 Introduction 1 2 Background 1 2.1 Bridging Descriptions . . . . . . . . . . . . . . . . . . . . . . . 1 2.2 The System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 HighDimensional Space . . . . . . . . . . . . . . . . . . . . . 5 2.3.1 Creating highdimensional space
Finding kdominant skylines in high dimensional space
 SIGMOD
"... Given a ddimensional data set, a point p dominates another point q if it is better than or equal to q in all dimensions and better than q in at least one dimension. A point is a skyline point if there does not exists any point that can dominate it. Skyline queries, which return skyline points, are ..."
Abstract

Cited by 74 (9 self)
 Add to MetaCart
points in high dimensional space, we propose a new concept, called kdominant skyline which relaxes the idea of dominance to kdominance. A point p is said to kdominate another point q if there are k ( ≤ d) dimensions in which p is better than or equal to q and is better in at least one of these k
Results 1  10
of
3,023,752