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1995
Principal component analysis is the most widely used method of multivariate data analysis owing to the simplicity of its algebra and to its straightforward interpretation.
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Principal component analysis is the most widely used method of multivariate data analysis owing to the simplicity of its algebra and to its straightforward interpretation.
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1983
A concept that is closely related to linear regression (preceding chapter) is principal components [15.1]. Linear regression addressed the question of how to fit a curve to one set of data, using a minimum number of factors. By contrast, the principal components problem asks how to fit many sets of data with a minimum number of curves.
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A concept that is closely related to linear regression (preceding chapter) is principal components [15.1]. Linear regression addressed the question of how to fit a curve to one set of data, using a minimum number of factors. By contrast, the principal components problem asks how to fit many sets of data with a minimum number of curves.
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2009
In addition to the autoregressive models described above, which are used for instance in the form of GARCH models when modeling volatility, a further technique of time .series analysis, called principal component analysis (abbreviated as PCA), is widely applied in the financial world.
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In addition to the autoregressive models described above, which are used for instance in the form of GARCH models when modeling volatility, a further technique of time .series analysis, called principal component analysis (abbreviated as PCA), is widely applied in the financial world.
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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
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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
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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
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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
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Kernel Principal Component Analysis
1997A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Schölkopf, B., Smola, A., Müller, K.
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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
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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
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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
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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
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Journal of the American Statistical Association, 2003
(2003). Principal Component Analysis. Journal of the American Statistical Association: Vol. 98, No. 464, pp. 1082-1083.
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(2003). Principal Component Analysis. Journal of the American Statistical Association: Vol. 98, No. 464, pp. 1082-1083.
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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 ...
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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 ...
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