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» The intrinsic dimensionality of graphs
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APVIS
2010
13 years 11 months ago
GMap: Visualizing graphs and clusters as maps
Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through...
Emden R. Gansner, Yifan Hu, Stephen G. Kobourov
JGT
2007
112views more  JGT 2007»
13 years 9 months ago
The 2-dimensional rigidity of certain families of graphs
Laman’s characterization of minimally rigid 2-dimensional generic frameworks gives a matroid structure on the edge set of the underlying graph, as was first pointed out and expl...
Bill Jackson, Brigitte Servatius, Herman Servatius
SDM
2007
SIAM
126views Data Mining» more  SDM 2007»
13 years 11 months 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
PCM
2004
Springer
103views Multimedia» more  PCM 2004»
14 years 3 months ago
Spectral Coding of Three-Dimensional Mesh Geometry Information Using Dual Graph
In this paper, we propose a new scheme for the geometry coding of three-dimensional (3-D) mesh models using a dual graph. In order to compress the mesh geometry information, we gen...
Sung-Yeol Kim, Seung-Uk Yoon, Yo-Sung Ho
IBPRIA
2003
Springer
14 years 3 months ago
Supervised Locally Linear Embedding Algorithm for Pattern Recognition
The dimensionality of the input data often far exceeds their intrinsic dimensionality. As a result, it may be difficult to recognize multidimensional data, especially if the number...
Olga Kouropteva, Oleg Okun, Matti Pietikäinen