88 search results - page 10 / 18 » Distance Preserving Dimension Reduction for Manifold Learnin... |
CVPR
2007
IEEE
15 years 11 months ago
2007
IEEE
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to gene...
PKDD
2010
Springer
14 years 7 months ago
2010
Springer
The foremost nonlinear dimensionality reduction algorithms provide an embedding only for the given training data, with no straightforward extension for test points. This shortcomin...
CVPR
2010
IEEE
15 years 5 months ago
2010
IEEE
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
COMPGEOM
2007
ACM
15 years 1 months ago
2007
ACM
In this paper we show that dimensionality reduction (i.e., Johnson-Lindenstrauss lemma) preserves not only the distances between static points, but also between moving points, and...