Results 1 to 10 of about 2,775,431 (371)

Nonlinear principal component analysis using autoassociative neural networks

, 1991
Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis.
M. Kramer
semanticscholar   +1 more source

Principal Components Analysis

2005
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

The biplot graphic display of matrices with application to principal component analysis

, 1971
SUMMARY Any matrix of rank two can be displayed as a biplot which consists of a vector for each row and a vector for each column, chosen so that any element of the matrix is exactly the inner product of the vectors corresponding to its row and to its ...
K. R. Gabriel
semanticscholar   +1 more source

Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019
This paper represents an implementation of Principal Component Analysis (PCA) on masked and non-masked face recognition. Security is an essential term in our today’s life.
Md. Sabbir Ejaz, M. Islam, Md Sifatullah, Ananya Sarker   +3 more
semanticscholar   +1 more source

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
openaire   +4 more sources

Principal Component Analysis [PDF]

, 2012
Among linear DR methods, principal component analysis (PCA) perhaps is the most important one. In linear DR, the dissimilarity of two points in a data set is defined by the Euclidean distance between them, and correspondingly, the similarity is described by their inner product.
Panos M. Pardalos, Panos M. Pardalos, Theodore B. Trafalis, Petros Xanthopoulos   +3 more
openaire   +3 more sources

Principal Component Analysis

2011
This chapter explains the theory of Principal component analysis (PCA) in detail and presents practical implementation issues along with various application examples. It introduces the mathematical concepts behind PCA such as mean value, covariance, eigenvalues, and eigenvectors. The principal components are ordered in way that the principal components
Anastasios Tefas, Ioannis Pitas
openaire   +2 more sources

Principal Component Analysis

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.
openaire   +2 more sources

Principal Components Analysis

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.
openaire   +2 more sources

Principal Component Analysis

2013
Most signal-processing problems can be reduced to some form of eigenvalue or singular-value problems.
M. N. S. Swamy, Ke-Lin Du
openaire   +2 more sources