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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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1986
The theory and practice of principal components are considered both from the point of view of statistical theory and from that of descriptive statistics. Some well known applications are briefly discussed.
Kloek, T., Kloek, T.
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The theory and practice of principal components are considered both from the point of view of statistical theory and from that of descriptive statistics. Some well known applications are briefly discussed.
Kloek, T., Kloek, T.
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Principal Component Adversarial Example
IEEE Transactions on Image Processing, 2020Despite having achieved excellent performance on various tasks, deep neural networks have been shown to be susceptible to adversarial examples, i.e., visual inputs crafted with structural imperceptible noise. To explain this phenomenon, previous works implicate the weak capability of the classification models and the difficulty of the classification ...
Yonggang Zhang +4 more
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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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Robust Principal Components Regression
2002We consider the multivariate linear regression model with p explanatory variables X and q ≥ 1 response variables Y. Moreover we assume that the regressors are multicollinear. This situation often occurs in the calibration of chemometrical data, where the X-variables correspond with spectra that are measured at many frequencies.
Verboven, Sabine, Hubert, Mia
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Journal of Affective Disorders, 2003
An alternative to the categorical classification of psychiatric diseases is the dimensional study of the signs and symptoms of psychiatric syndromes. To date, there have been few reports about the dimensions of mania, and the existence of a depressive dimension in mania remains controversial.
A, González-Pinto +6 more
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An alternative to the categorical classification of psychiatric diseases is the dimensional study of the signs and symptoms of psychiatric syndromes. To date, there have been few reports about the dimensions of mania, and the existence of a depressive dimension in mania remains controversial.
A, González-Pinto +6 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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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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2012
Principal Components are probably the best known and most widely used of all multivariate analysis techniques. The essential idea consists in performing a linear transformation of the observed k-dimensional variables in such a way that the new variables are vectors of k mutually orthogonal (uncorrelated) components – the principal components – ranked ...
Hallin, Marc, Hörmann, Siegfried
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Principal Components are probably the best known and most widely used of all multivariate analysis techniques. The essential idea consists in performing a linear transformation of the observed k-dimensional variables in such a way that the new variables are vectors of k mutually orthogonal (uncorrelated) components – the principal components – ranked ...
Hallin, Marc, Hörmann, Siegfried
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