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Multilinear subspace learning

Known as: Multilinear subspace, Tensor subspace learning 
Multilinear subspace learning is an approach to dimensionality reduction. Dimensionality reduction can be performed on data tensor whose observations… 
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Related topics

Related topics

30 relations
Artificial neural networkBig dataComputer data storageCurse of dimensionality

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
ON LINEARLY TOPOLOGIZED MODULES OVER AN ARBITRARY RING
Fundamental constructions in the category of linearly topologized modules over an arbitrary ring are studied, as well as certain… 
2014
2014
Multipath subspace pursuit for compressive sensing signal reconstruction
This paper proposes a greedy reconstruction algorithm to recover sparse signal from compressed measurements, called multipath… 
2012
2012
Analysis of the Deflated Conjugate Gradient Method Based on Symmetric Multigrid Theory
Deflation techniques for Krylov subspace methods and in particular the conjugate gradient method have seen a lot of attention in… 
2011
2011
The multifactor extension of Grassmann manifolds for face recognition
We propose the use of a multifactor model that extends Grassmann manifold to multiple factor frameworks. Both manifold learning… 
2009
2009
On Characterizations of the Space of -Semi-Integral Multilinear Mappings
In this paper we consider the ideal of -semi-integral -linear mappings, which is a natural multilinear extension of theideal of… 
2008
2008
Face Recognition by Discriminative Orthogonal Rank-one Tensor Decomposition
Discriminative subspace analysis has been a popular approach to face recognition. Most of the previous work such as Eigen-faces… 
2007
2007
Uncorrelated Multilinear Discriminant Analysis with Regularization for Gait Recognition
This paper proposes a novel uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait… 
2006
2006
A new adaptive control algorithm for systems with multilinear parametrization
Adaptive control of nonlinearly parametrized (NLP) systems is an unknown field, where few results have been proposed up to now… 
2006
2006
Applying Ensembles of Multilinear Classifiers in the Frequency Domain
Ensemble methods such as bootstrap, bagging or boosting have had a considerable impact on recent developments in machine learning… 
Highly Cited
2003
Highly Cited
2003
Cotype and absolutely summing homogeneous polynomials in ${\cal L}_{p}$ spaces
We lift to homogeneous polynomials and multilinear mappings a linear result due to Lindenstrauss and Pe"czy nski for absolutely… 
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