71 search results - page 2 / 15 » Unsupervised Learning of Manifolds via Linear Approximations |
ICIP
2008
IEEE
14 years 7 months ago
2008
IEEE
Subspace learning techniques are widespread in pattern recognition research. They include PCA, ICA, LPP, etc. These techniques are generally linear and unsupervised. The problem o...
ICCV
2007
IEEE
13 years 11 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
CVPR
2007
IEEE
14 years 7 months ago
2007
IEEE
We propose a novel algorithm for segmenting multiple motions of different types from point correspondences in multiple affine or perspective views. Since point trajectories associ...
CVPR
2007
IEEE
14 years 7 months ago
2007
IEEE
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...
ICIP
2007
IEEE
13 years 9 months ago
2007
IEEE
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables t...