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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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Segmented principal component transform–principal component analysis
Chemometrics and Intelligent Laboratory Systems, 2005Abstract A new approach to perform Principal Component Analysis (PCA) on very wide matrices is proposed in this work. The procedure is based on an extension of the Principal Component Transform (PCT) concept—the PCT being applied to non-superimposed segments of the data matrix.
António S. Barros, Douglas N. Rutledge
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2022
Today, cryptocurrencies are rapidly gaining popularity and sweeping all the economies of the world, but the bulk of the literature is devoted to a few cryptocurrencies only. The purpose of this chapter is to analyze of the cryptocurrency market. More than 2000 cryptocurrencies are examined, and a set of 70 cryptocurrencies were recovered for a sample ...
Nabiha Haouas, Asma Sghaier
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Today, cryptocurrencies are rapidly gaining popularity and sweeping all the economies of the world, but the bulk of the literature is devoted to a few cryptocurrencies only. The purpose of this chapter is to analyze of the cryptocurrency market. More than 2000 cryptocurrencies are examined, and a set of 70 cryptocurrencies were recovered for a sample ...
Nabiha Haouas, Asma Sghaier
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Introduction to Principal Components Analysis
PM&R, 2014Principal components analysis (PCA) is a powerful statistical tool that can help researchers analyze datasets with many highly related predictors. PCA is a data reduction technique— that is, it reduces a larger set of predictor variables to a smaller set with minimal loss of information.
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Principal components for allometric analysis
American Journal of Physical Anthropology, 1983AbstractLogarithmic bivariate regression slopes and logarithmic principal component coefficient ratios are two methods for estimating allometry coefficients corresponding to a in the classic power formula Y = BXa. Both techniques depend on high correlation between variables.
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Principal Component Discriminant Analysis
Statistical Applications in Genetics and Molecular Biology, 2008The approach adopted involved two-stages. First the 11205 measurements in the mass spectrometry data were reduced to 14 scores by a principal component analysis of the centered but otherwise untreated and unscaled data matrix. Then a linear classifier was derived by linear discriminant analysis using these 14 scores as inputs. This number of scores was
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2011
One of the problems with a lot of sets of multivariate data is that there are simply too many variables to make the application of the graphical techniques described in the previous chapters successful in providing an informative initial assessment of the data. And having too many variables can also cause problems for other multivariate techniques that
Brian Everitt, Torsten Hothorn
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One of the problems with a lot of sets of multivariate data is that there are simply too many variables to make the application of the graphical techniques described in the previous chapters successful in providing an informative initial assessment of the data. And having too many variables can also cause problems for other multivariate techniques that
Brian Everitt, Torsten Hothorn
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Influence in principal components analysis
Biometrika, 1985To detect outliers in data analyzed for principal components, three types of influence functions are derived based on perturbation parameters. Influence of these functions on the eigenvalues and eigenvectors of the covariance matrix is examined. The functions are compared among themselves and are contrasted with the regression case.
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A generalization of the principal component analysis
2007A nonlinear generalization of the principal component analysis (PCA) is made under normality. It is shown that this generalized PCA problem leads to an eigenvalue problem for the Hadamard products of the correlation matrix. In the framework of the generalized PCA, the result is applied to the problem of finding square-integrable continuous ...
Kariya Takeaki +2 more
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