540 search results - page 11 / 108 » Dimensionality Reduction for Data in Multiple Feature Repres... |
ICML
2005
IEEE
14 years 7 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...
GRC
2010
IEEE
13 years 7 months ago
2010
IEEE
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
ICASSP
2011
IEEE
12 years 10 months ago
2011
IEEE
Recently, in generic object recognition research, a classification technique based on integration of image features is garnering much attention. However, with a classifying techn...
WACV
2008
IEEE
14 years 1 months ago
2008
IEEE
Most face recognition algorithms use a “distancebased” approach: gallery and probe images are projected into a low dimensional feature space and decisions about matching are b...
ICML
2005
IEEE
14 years 7 months ago
2005
IEEE
Dimensionality reduction is the problem of finding a low-dimensional representation of highdimensional input data. This paper examines the case where additional information is kno...