434 search results - page 25 / 87 » Dimensionality Reduction of Clustered Data Sets |
ICIP
2010
IEEE
13 years 6 months ago
2010
IEEE
Many classes of image data span a low dimensional nonlinear space embedded in the natural high dimensional image space. We adopt and generalize a recently proposed dimensionality ...
CORR
2010
Springer
13 years 7 months ago
2010
Springer
This paper discusses the topic of dimensionality reduction for k-means clustering. We prove that any set of n points in d dimensions (rows in a matrix A ∈ Rn×d ) can be project...
GFKL
2004
Springer
14 years 2 months ago
2004
Springer
Abstract. A method for measuring the density of data sets that contain an unknown number of clusters of unknown sizes is proposed. This method, called Pareto Density Estimation (PD...
MICCAI
2004
Springer
14 years 9 months ago
2004
Springer
Abstract. With the increasing capability of MR imaging and Computational Fluid Dynamics (CFD) techniques, a significant amount of data related to the haemodynamics of the cardiovas...
PAMI
2006
13 years 8 months ago
2006
The k-means algorithm is widely used for clustering because of its computational efficiency. Given n points in d-dimensional space and the number of desired clusters k, k-means see...