99 search results - page 1 / 20 » Analysis and extension of spectral methods for nonlinear dim... |
ICML
2005
IEEE
14 years 5 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...
BMCBI
2005
13 years 4 months ago
2005
Background: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks ...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spect...
ICML
2010
IEEE
13 years 6 months ago
2010
IEEE
We propose a new dimensionality reduction method, the elastic embedding (EE), that optimises an intuitive, nonlinear objective function of the low-dimensional coordinates of the d...
PKDD
2004
Springer
13 years 10 months ago
2004
Springer
We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certai...