Add to ORKGAbstract – Principal Component Analysis (PCA) is a well known statistical method that has successfully been applied for reducing data dimensionality. Focusing on a neural network which approximates the results obtained by classical PCA, the main contribution of this work consists in introducing a parallel modeling for such network. A comparative study shows that the proposal presents promising results when a multi-core computer is available
Local Principal Components Analysis, i.e. Principal Component Analysis performed in data clusters, i...
One of the most commonly known algorithm to perform neural Principal Component Analysis of real-valu...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
Abstract. Principal component analysis allows the identification of a linear transformation such tha...
Machine learning consists in the creation and development of algorithms that allow a machine to lear...
Abstract—Human face is contexture multidimensional point of vision model and by creating computation...
Copyright © 2014 Kanokmon Rujirakul et al. This is an open access article distributed under the Crea...
Classical feature extraction and data projection methods have been extensively investigated in the p...
Classical feature extraction and data projection methods have been extensively investigated in the p...
Abstract. Face recognition is one of the most important image processing research topics which is wi...
AbstractA principal component analysis (PCA) neural network is developed for online extraction of th...
Neural Network and PCA technique for the detection of the persons. I have used the PCA technique whi...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
This book not only provides a comprehensive introduction to neural-based PCA methods in control scie...
This study introduces a novel fine-grained parallel implementation of a neural principal component a...
Local Principal Components Analysis, i.e. Principal Component Analysis performed in data clusters, i...
One of the most commonly known algorithm to perform neural Principal Component Analysis of real-valu...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
Abstract. Principal component analysis allows the identification of a linear transformation such tha...
Machine learning consists in the creation and development of algorithms that allow a machine to lear...
Abstract—Human face is contexture multidimensional point of vision model and by creating computation...
Copyright © 2014 Kanokmon Rujirakul et al. This is an open access article distributed under the Crea...
Classical feature extraction and data projection methods have been extensively investigated in the p...
Classical feature extraction and data projection methods have been extensively investigated in the p...
Abstract. Face recognition is one of the most important image processing research topics which is wi...
AbstractA principal component analysis (PCA) neural network is developed for online extraction of th...
Neural Network and PCA technique for the detection of the persons. I have used the PCA technique whi...
In statistical practice multicollinearity of predictor variables is rather the rule than the excepti...
This book not only provides a comprehensive introduction to neural-based PCA methods in control scie...
This study introduces a novel fine-grained parallel implementation of a neural principal component a...
Local Principal Components Analysis, i.e. Principal Component Analysis performed in data clusters, i...
One of the most commonly known algorithm to perform neural Principal Component Analysis of real-valu...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...