79 search results - page 1 / 16 » Automatic configuration of spectral dimensionality reduction... |
PRL
2010
13 years 6 months ago
2010
In this paper, our main contribution is a framework for the automatic configuration of any spectral dimensionality reduction methods. This is achieved, first, by introducing the m...
PKDD
2004
Springer
14 years 27 days 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...
ICML
2005
IEEE
14 years 8 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...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenm...
ICML
2007
IEEE
14 years 8 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...