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2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
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Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
openaire +3 more sources
Directed Principal Component Analysis
Operations Research, 2014We consider a problem involving estimation of a high-dimensional covariance matrix that is the sum of a diagonal matrix and a low-rank matrix, and making a decision based on the resulting estimate. Such problems arise, for example, in portfolio management, where a common approach employs principal component analysis (PCA) to estimate factors used in ...
Kao, Yi-Hao, Van Roy, Benjamin
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Principal Components Analysis Competitive Learning
Neural Computation, 2004We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while
López-Rubio, Ezequiel +3 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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Robust Kernel Principal Component Analysis
Neural Computation, 2009This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions
Huang S.-Y., Yeh Y.-R., Eguchi S.
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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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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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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
openaire +1 more source
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
openaire +2 more sources