Multilinear principal component analysis

Known as: Multilinear PCA, Multilinear principal-component analysis 
Multilinear Principal Component Analysis (MPCA) is a multilinear extension of principal component analysis (PCA). MPCA is employed in the analysis of… (More)
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Topic mentions per year

Topic mentions per year

2006-2018
051020062018

Papers overview

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2014
2014
In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data… (More)
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2011
2011
This study establishes the mathematical foundation for a fast incremental multilinear method which combines the traditional… (More)
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2010
2010
In this study, a method is proposed based on multilinear principal component analysis (MPCA) for face recognition. This method… (More)
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2010
2010
A new batch process monitoring based on Multilinear Principal Component Analysis (MLPCA) is proposed in this paper. In the… (More)
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2009
2009
This paper proposes an uncorrelated multilinear principal component analysis (UMPCA) algorithm for unsupervised subspace learning… (More)
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2009
2009
We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional… (More)
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Highly Cited
2008
Highly Cited
2008
This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects… (More)
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2008
2008
Motivated by the application of the 2D principal component analysis (PCA) for face recognition, this study proposes a modified… (More)
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2008
2008
Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their… (More)
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2006
2006
In this paper, a multilinear formulation of the popular principal component analysis (PCA) is proposed, named as multilinear PCA… (More)
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