Results 331 to 340 of about 2,775,431 (371)
Some of the next articles are maybe not open access.
1980
In order to examine the relationships among a set of p correlated variables, it may be useful to transform the original set of variables to a new set of uncorrelated variables called principal components. These new variables are linear combinations of the original variables and are derived in decreasing order of importance so that, for example, the ...
Chris Chatfield, Alexander J. Collins
openaire +2 more sources
In order to examine the relationships among a set of p correlated variables, it may be useful to transform the original set of variables to a new set of uncorrelated variables called principal components. These new variables are linear combinations of the original variables and are derived in decreasing order of importance so that, for example, the ...
Chris Chatfield, Alexander J. Collins
openaire +2 more sources
2008
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns in multivariate data. It aims to graphically display the relative positions of data points in fewer dimensions while retaining as much information as possible, and explore relationships between dependent variables. It is a hypothesis-generating technique
openaire +2 more sources
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns in multivariate data. It aims to graphically display the relative positions of data points in fewer dimensions while retaining as much information as possible, and explore relationships between dependent variables. It is a hypothesis-generating technique
openaire +2 more sources
2000
By projecting the data into a lower dimensional space that accurately characterizes the state of the process, dimensionality reduction techniques can greatly simplify and improve process monitoring procedures. Principal Component Analysis (PCA) is such a dimensionality reduction technique.
Leo H. Chiang +2 more
openaire +2 more sources
By projecting the data into a lower dimensional space that accurately characterizes the state of the process, dimensionality reduction techniques can greatly simplify and improve process monitoring procedures. Principal Component Analysis (PCA) is such a dimensionality reduction technique.
Leo H. Chiang +2 more
openaire +2 more sources
Sparse Principal Component Analysis With Preserved Sparsity Pattern
IEEE Transactions on Image Processing, 2019Principal component analysis (PCA) is widely used for feature extraction and dimension reduction in pattern recognition and data analysis. Despite its popularity, the reduced dimension obtained from the PCA is difficult to interpret due to the dense ...
A. Seghouane, Navid Shokouhi, Inge Koch
semanticscholar +1 more source
2019
Principal component analysis (PCA) is a statistical process that allows reducing number of variables from a given dataset to a smaller set of variables that can be used in data analysis. The reduced set of variables retain the variance present in the original dataset.
Ajay Ravi +2 more
openaire +2 more sources
Principal component analysis (PCA) is a statistical process that allows reducing number of variables from a given dataset to a smaller set of variables that can be used in data analysis. The reduced set of variables retain the variance present in the original dataset.
Ajay Ravi +2 more
openaire +2 more sources
Feature Selection for Classification using Principal Component Analysis and Information Gain
Expert systems with applications, 2021E. Omuya, G. Okeyo, Michael W. Kimwele
semanticscholar +1 more source
2004
Principal components analysis is a way of reducing the number of variables in the model. It may be that some of the variables are highly correlated with each other, so that not all are needed for a description of the subject of study; perhaps a few linear combinations of the variables would suffice.
Andrea Cerioli +2 more
openaire +2 more sources
Principal components analysis is a way of reducing the number of variables in the model. It may be that some of the variables are highly correlated with each other, so that not all are needed for a description of the subject of study; perhaps a few linear combinations of the variables would suffice.
Andrea Cerioli +2 more
openaire +2 more sources
1989
Introduction Basic Concepts of Principal Components Geometrical Properties of Principal Components Decomposition Properties of Principal Components Principal Components of Patterned Correlation Matrices Rotation of Principal Components Using Principal Components to Select a Subset of Variables Principal Components Versus Factor Analysis Uses of ...
openaire +2 more sources
Introduction Basic Concepts of Principal Components Geometrical Properties of Principal Components Decomposition Properties of Principal Components Principal Components of Patterned Correlation Matrices Rotation of Principal Components Using Principal Components to Select a Subset of Variables Principal Components Versus Factor Analysis Uses of ...
openaire +2 more sources
Issues and recommendations for exploratory factor analysis and principal component analysis.
Research in Social and Administrative Pharmacy, 2020James B. Schreiber
semanticscholar +1 more source