434 search results - page 50 / 87 » Dimensionality Reduction of Clustered Data Sets |
160
click to vote
MLDM
2005
Springer
15 years 11 months ago
2005
Springer
In this paper we describe a new cluster model which is based on the concept of linear manifolds. The method identifies subsets of the data which are embedded in arbitrary oriented...
166
click to vote
CCGRID
2006
IEEE
15 years 9 months ago
2006
IEEE
P2P systems represent a large portion of the Internet traffic which makes the data discovery of great importance to the user and the broad Internet community. Hence, the power of ...
145
click to vote
IJCAI
2007
15 years 7 months ago
2007
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
212
click to vote
FOCS
2000
IEEE
15 years 10 months ago
2000
IEEE
In this article, we show several results obtained by combining the use of stable distributions with pseudorandom generators for bounded space. In particular: —We show that, for a...
140
click to vote
COMPGEOM
2009
ACM
16 years 3 days ago
2009
ACM
Nonlinear dimensionality reduction (NLDR) algorithms such as Isomap, LLE and Laplacian Eigenmaps address the problem of representing high-dimensional nonlinear data in terms of lo...