Sciweavers

Share
ISVC
2010
Springer
8 years 9 months ago
Combining Automated and Interactive Visual Analysis of Biomechanical Motion Data
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...
Scott Spurlock, Remco Chang, Xiaoyu Wang, George A...
PR
2006
147views more  PR 2006»
8 years 11 months ago
Robust locally linear embedding
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...
Hong Chang, Dit-Yan Yeung
PAMI
2006
127views more  PAMI 2006»
8 years 11 months ago
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
Understanding the structure of multidimensional patterns, especially in unsupervised case, is of fundamental importance in data mining, pattern recognition and machine learning. Se...
Martin H. C. Law, Anil K. Jain
NIPS
1997
9 years 20 days ago
Mapping a Manifold of Perceptual Observations
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...
Joshua B. Tenenbaum
NIPS
2003
9 years 21 days ago
Optimal Manifold Representation of Data: An Information Theoretic Approach
We introduce an information theoretic method for nonparametric, nonlinear dimensionality reduction, based on the infinite cluster limit of rate distortion theory. By constraining...
Denis V. Chigirev, William Bialek
NIPS
2003
9 years 21 days ago
Minimax Embeddings
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...
Matthew Brand
IJCAI
2003
9 years 21 days ago
Continuous nonlinear dimensionality reduction by kernel Eigenmaps
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...
Matthew Brand
SDM
2007
SIAM
126views Data Mining» more  SDM 2007»
9 years 23 days ago
Nonlinear Dimensionality Reduction using Approximate Nearest Neighbors
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...
Erion Plaku, Lydia E. Kavraki
ESANN
2008
9 years 24 days ago
Rank-based quality assessment of nonlinear dimensionality reduction
Abstract. Nonlinear dimensionality reduction aims at providing lowdimensional representions of high-dimensional data sets. Many new methods have been proposed in the recent years, ...
John Aldo Lee, Michel Verleysen
AUSAI
2006
Springer
9 years 3 months ago
Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data
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...
Yi Guo, Junbin Gao, Paul Wing Hing Kwan
books