ISVC
2010
Springer
13 years 10 months ago
2010
Springer
Abstract. We present a framework for combining automated and interactive visual analysis techniques for use on high-resolution biomechanical data. Analyzing the complex 3D motion o...
PR
2006
13 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...
PAMI
2006
13 years 11 months ago
2006
Understanding the structure of multidimensional patterns, especially in unsupervised case, is of fundamental importance in data mining, pattern recognition and machine learning. Se...
NIPS
1997
14 years 23 days ago
1997
Nonlinear dimensionality reduction is formulated here as the problem of trying to find a Euclidean feature-space embedding of a set of observations that preserves as closely as p...
NIPS
2003
14 years 24 days ago
2003
We introduce an information theoretic method for nonparametric, nonlinear dimensionality reduction, based on the infinite cluster limit of rate distortion theory. By constraining...
NIPS
2003
14 years 24 days ago
2003
Spectral methods for nonlinear dimensionality reduction (NLDR) impose a neighborhood graph on point data and compute eigenfunctions of a quadratic form generated from the graph. W...
IJCAI
2003
14 years 24 days ago
2003
We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
SDM
2007
SIAM
14 years 26 days ago
2007
SIAM
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-d...
ESANN
2008
14 years 27 days ago
2008
Abstract. Nonlinear dimensionality reduction aims at providing lowdimensional representions of high-dimensional data sets. Many new methods have been proposed in the recent years, ...
AUSAI
2006
Springer
14 years 3 months ago
2006
Springer
In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data...