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» The intrinsic dimensionality of graphs
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NIPS
2003
15 years 1 months 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
COMGEO
2004
ACM
14 years 11 months ago
A multi-dimensional approach to force-directed layouts of large graphs
We present a novel hierarchical force-directed method for drawing large graphs. Given a graph G = (V,E), the algorithm produces an embedding for G in an Euclidean space E of any d...
Pawel Gajer, Michael T. Goodrich, Stephen G. Kobou...
IROS
2007
IEEE
136views Robotics» more  IROS 2007»
15 years 6 months ago
Task-induced symmetry and reduction in kinematic systems with application to needle steering
— Lie group symmetry in a mechanical system can lead to a dimensional reduction in its dynamical equations. Typically, the symmetries that one exploits are intrinsic to the mecha...
Vinutha Kallem, Dong Eui Chang, Noah J. Cowan
PAMI
2011
14 years 6 months ago
Kernel Optimization in Discriminant Analysis
— Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separ...
Di You, Onur C. Hamsici, Aleix M. Martínez
IVC
2007
164views more  IVC 2007»
14 years 11 months ago
Locality preserving CCA with applications to data visualization and pose estimation
- Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality reduction and has been applied to image processing, pose estimation and other fields. H...
Tingkai Sun, Songcan Chen