Computer Science
Accuracy
18%
Algorithms
9%
Analysis Method
18%
Component Analysis
100%
Decomposition Matrix
18%
Dimension Reduction
100%
Dimensional Problem
9%
Engineering Problem
9%
Experimental Result
9%
Feature Selection
100%
Feature Selection
9%
Functions
9%
Least Squares Methods
9%
Linear Relationship
9%
Low-Rank
9%
Models
9%
Objective Function
18%
Preprocessing
18%
Principal Component
100%
Processing Method
9%
Real Data Sets
9%
Reduction Variable
9%
Regularization
9%
Scientific Problem
9%
Selection Strategy
9%
Sparsity
18%
Training Sample
9%
Vectors
18%
Earth and Planetary Sciences
Accuracy
18%
Algorithms
9%
Analogy
9%
Comparison
9%
Criterion
9%
Data Set
9%
Decomposition
18%
Dimension
100%
Input
18%
Model
9%
Norm
9%
Optimization
9%
Preprocessing
18%
Principal Components Analysis
100%
Response
9%
Sample
9%
Selection
100%
Shape
9%
Show
18%
Square
9%
Strategy
18%
Target
9%
Tradeoff
9%
Vector
18%
Mathematics
Data Set
9%
Decomposition Algorithms
9%
Dimensional Problem
9%
Eigenvector
18%
Independence
9%
Linear Models
9%
Loss Function
9%
Matrix
9%
Matrix Decomposition
18%
Nonlinear Relationship
9%
Number
9%
Objective Function
18%
Partial Least Squares
9%
PCA
9%
Principal Component Analysis
100%
Real Data
9%
Regularization
9%
Selection
100%
Simulated Data
9%
Tradeoff
9%
Training Sample
9%
Agricultural and Biological Sciences
Accuracy
18%
Algorithms
9%
Dimensions
100%
Engineering
9%
Least Squares
9%
Linear Models
9%
Objectives
36%
Principal Component Analysis
100%
Solutions
9%