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BMCBI
2010
113views more  BMCBI 2010»
10 years 1 months ago
Probabilistic Principal Component Analysis for Metabolomic Data
Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique fo...
Gift Nyamundanda, Lorraine Brennan, Isobel Claire ...
TIT
2002
72views more  TIT 2002»
10 years 1 months ago
Principal curves with bounded turn
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...
S. Sandilya, Sanjeev R. Kulkarni
PAMI
2002
122views more  PAMI 2002»
10 years 1 months ago
Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object
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...
Ravi Ramamoorthi
NECO
1998
151views more  NECO 1998»
10 years 1 months ago
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
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...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
IDA
1998
Springer
10 years 1 months ago
Fast Dimensionality Reduction and Simple PCA
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...
Matthew Partridge, Rafael A. Calvo
TSMC
2008
95views more  TSMC 2008»
10 years 1 months ago
Natural Movement Generation Using Hidden Markov Models and Principal Components
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...
Junghyun Kwon, Frank C. Park
PRL
2006
225views more  PRL 2006»
10 years 1 months ago
A straight line detection using principal component analysis
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...
Yun-Seok Lee, Han-Suh Koo, Chang-Sung Jeong
JASIS
2006
90views more  JASIS 2006»
10 years 1 months ago
Can scientific journals be classified in terms of aggregated journal-journal citation relations using the Journal Citation Repor
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...
Loet Leydesdorff
BMCBI
2007
149views more  BMCBI 2007»
10 years 1 months ago
Robust imputation method for missing values in microarray data
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
Dankyu Yoon, Eun-Kyung Lee, Taesung Park
CSDA
2006
304views more  CSDA 2006»
10 years 1 months ago
Using principal components for estimating logistic regression with high-dimensional multicollinear data
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...
Ana M. Aguilera, Manuel Escabias, Mariano J. Valde...
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