263 search results - page 11 / 53 » The Global Dimensionality of Face Space |
TNN
2008
13 years 8 months ago
2008
Abstract--Plenty of methods have been proposed in order to discover latent variables (features) in data sets. Such approaches include the principal component analysis (PCA), indepe...
BMVC
2010
13 years 6 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...
NIPS
2004
13 years 9 months ago
2004
Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional d...
SADM
2008
13 years 7 months ago
2008
: In this article, we propose an efficient and effective method for finding arbitrarily oriented subspace clusters by mapping the data space to a parameter space defining the set o...
CIKM
2008
Springer
13 years 10 months ago
2008
Springer
Finding latent patterns in high dimensional data is an important research problem with numerous applications. The most well known approaches for high dimensional data analysis are...