325 search results - page 10 / 65 » Statistical Properties of Kernel Principal Component Analysi... |
CVPR
2003
IEEE
14 years 9 months ago
2003
IEEE
Principal component analysis has proven to be useful for understanding geometric variability in populations of parameterized objects. The statistical framework is well understood ...
MIAR
2006
IEEE
14 years 1 months ago
2006
IEEE
Abstract. A main focus of statistical shape analysis is the description of variability of a population of geometric objects. In this paper, we present work in progress towards mode...
ICIP
2008
IEEE
14 years 9 months ago
2008
IEEE
This paper proposes a new robust 3-D object blind watermarking method using constraints in the spectral domain. Mesh watermarking in spectral domain has the property of spreading ...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
ECCV
2002
Springer
14 years 9 months ago
2002
Springer
Abstract. Principal Component Analysis (PCA) is one of the most popular techniques for dimensionality reduction of multivariate data points with application areas covering many bra...