NIPS
2000
13 years 6 months ago
2000
A central issue in principal component analysis (PCA) is choosing the number of principal components to be retained. By interpreting PCA as density estimation, this paper shows ho...
BMVC
1998
13 years 6 months ago
1998
A method of extracting, classifying and modelling non-rigid shapes from an image sequence is presented. Shapes are approximated by polygons where the number of sides is related to...
SIMVIS
2004
13 years 6 months ago
2004
We describe methods for the specification and modification of virtual resections in medical volume data. These techniques are focused on applications in therapy planning, but are a...
GRAPHICSINTERFACE
2003
13 years 6 months ago
2003
The creation of realistic 3D face models is still a fundamental problem in computer graphics. In this paper we present a novel method to obtain the 3D shape of an arbitrary human ...
ESANN
2006
13 years 6 months ago
2006
Blind source separation (BSS) has become one of the major signal and image processing area in many applications. Principal component analysis (PCA) and Independent component analys...
AAAI
2004
13 years 6 months ago
2004
Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection prob...
SDM
2007
SIAM
13 years 6 months ago
2007
SIAM
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace...
ICONIP
2007
13 years 6 months ago
2007
Abstract. Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solvi...
LREC
2010
13 years 6 months ago
2010
Since the first half of the 20th century, readability formulas have been widely employed to automatically predict the readability of an unseen text. In this article, the formulas ...
ICCV
1995
IEEE
13 years 8 months ago
1995
IEEE
A general-purpose object indexingtechnique is described that combines the virtues of principal component analysis with the favorable matching properties of high-dimensional spaces...