98 search results - page 8 / 20 » Regression on manifolds using kernel dimension reduction |
ICIP
2007
IEEE
13 years 10 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...
ICML
2007
IEEE
14 years 7 months ago
2007
IEEE
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...
ICCV
2007
IEEE
14 years 1 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...
ICPR
2008
IEEE
14 years 1 months ago
2008
IEEE
We propose a new method to retrieve similar face images from large face databases. The proposed method extracts a set of Haar-like features, and integrates these features with sup...
PAMI
2008
13 years 6 months ago
2008
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold...