396 search results - page 30 / 80 » Lossy Reduction for Very High Dimensional Data |
CAIP
1997
Springer
15 years 3 months ago
1997
Springer
Abstract. In this paper a new method for analyzing the intrinsic dimensionality (ID) of low dimensional manifolds in high dimensional feature spaces is presented. The basic idea is...
116
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CGF
1998
14 years 11 months ago
1998
A multiresolution data decomposition offers a fundamental framework supporting compression, progressive transmission, and level-of-detail (LOD) control for large two or three dime...
114
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PR
2006
14 years 11 months ago
2006
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning communit...
PR
2006
14 years 11 months ago
2006
All of the prototype reduction schemes (PRS) which have been reported in the literature, process time-invariant data to yield a subset of prototypes that are useful in nearest-nei...
ICML
2007
IEEE
16 years 18 days ago
2007
IEEE
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...