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Feature reduction using minimum noise fraction and principal component analysis transforms for improving the classification of hyperspectral image

, 2017
et.al Murinto Nur
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Principal Components Analysis

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., Hartmann, S., Klie, S., Selbig, J.   +3 more
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Coupled Principal Component Analysis

IEEE Transactions on Neural Networks, 2004
A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the
Möller, Ralf, Könies, Axel
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Segmented principal component transform–principal component analysis

Chemometrics and Intelligent Laboratory Systems, 2005
Abstract 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 COMPONENTS

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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Principal Component Adversarial Example

IEEE Transactions on Image Processing, 2020
Despite 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, Xinmei Tian, Ya Li, Xinchao Wang, Dacheng Tao   +4 more
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Directed Principal Component Analysis

Operations Research, 2014
We 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

2002
We 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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Principal components of mania

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, J, Ballesteros, A, Aldama, J L, Pérez de Heredia, M, Gutierrez, F, Mosquera, A, González-Pinto   +6 more
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