Amanote Research
Register
Sign In
Positive Semidefinite Matrix Completions on Chordal Graphs and Constraint Nondegeneracy in Semidefinite Programming
Linear Algebra and Its Applications
- Netherlands
doi 10.1016/j.laa.2008.10.010
Full Text
Open PDF
Abstract
Available in
full text
Categories
Numerical Analysis
Algebra
Combinatorics
Number Theory
Geometry
Discrete Mathematics
Topology
Date
February 1, 2009
Authors
Houduo Qi
Publisher
Elsevier BV
Related search
Table S2: Environmental Variables and Loadings on the First Two Principal Components (PC1 and PC2) Based on a Principal Component Analysis. Bold Text Indicates the Variables Used as Input for Maxent Modelling
Table 2: Principal Components (PC1 and PC2) Eigenvalues and PC Loadings for the CCA Parameters.
CPCA: A Program for Principal Component Analysis With External Information on Subjects and Variables
Behavior Research Methods, Instruments, & Computers
Figure 1: Principal Components (90 Observations, 20 Variables): First Three Principal Components Explains 54.3% of Total Variance.
Figure 4: (A) Principal Component Analysis (PC1 vs PC2) and Associated Patterns of Morphological Transformation for Crania of Five Dasypus Species. (B) Regression of the First Principal Component on the Logarithm of the Centroid Size (R2 = 0.50; P < 0.001).
Figure S4: Contribution of Spot Trait Variables to Principal Components Analysis First and Second Dimensions
Table S1: Loadings of Morphological Parameters With the First Three Rotated Principal Components (RC)
Figure 4: 3D Representation of the (A) Vegetation Plots and (B) Geo-Climatic Variables Along the Three First Principal Components of the Principal Component Analysis (Cumulative Variance Explained 76.9%) Conducted on the Páramo Vegetation Dataset.
Supplemental Information 2: Summary of Principal Components as Shown by the Principal Component Analysis.
The Analysis Meteorological Satellite Software Based on Principal Component
