46 search results - page 9 / 10 » Embedding ultrametrics into low-dimensional spaces |
79
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ICPR
2010
IEEE
15 years 1 days ago
2010
IEEE
—A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach,...
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
Recently, several manifold learning algorithms have been proposed, such as ISOMAP (Tenenbaum et al., 2000), Locally Linear Embedding (Roweis & Saul, 2000), Laplacian Eigenmap ...
FGR
2006
IEEE
15 years 3 months ago
2006
IEEE
Characteristics of the 2D shape deformation in human motion contain rich information for human identification and pose estimation. In this paper, we introduce a framework for sim...
117
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BIOINFORMATICS
2008
14 years 9 months ago
2008
Motivation: Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between...
BMVC
2010
14 years 7 months ago
2010
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...