BMCBI
2010
13 years 11 months ago
2010
Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique fo...
TIT
2002
13 years 11 months ago
2002
Principal curves, like principal components, are a tool used in multivariate analysis for ends like feature extraction. Defined in their original form, principal curves need not ex...
PAMI
2002
13 years 11 months ago
2002
We analyze theoretically the subspace best approximating images of a convex Lambertian object taken from the same viewpoint, but under different distant illumination conditions. Si...
NECO
1998
13 years 11 months ago
1998
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
IDA
1998
Springer
13 years 11 months ago
1998
Springer
A fast and simple algorithm for approximately calculating the principal components (PCs) of a data set and so reducing its dimensionality is described. This Simple Principal Compo...
TSMC
2008
13 years 11 months ago
2008
Recent studies have shown that the perception of natural movements--in the sense of being "humanlike"--depends on both joint and task space characteristics of the movemen...
PRL
2006
13 years 11 months ago
2006
A straight line detection algorithm is presented. The algorithm separates row and column edges from edge image using their primitive shapes. The edges are labeled, and the princip...
JASIS
2006
13 years 11 months ago
2006
The aggregated citation relations among journals included in the Science Citation Index provide us with a huge matrix which can be analyzed in various ways. Using principal compon...
BMCBI
2007
13 years 11 months ago
2007
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
CSDA
2006
13 years 11 months ago
2006
The logistic regression model is used to predict a binary response variable in terms of a set of explicative ones. The estimation of the model parameters is not too accurate and t...