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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.
Review
2018
Review
2018
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA
The widespread use of multisensor technology and the emergence of big data sets have brought the necessity to develop more… 
Highly Cited
2013
Highly Cited
2013
Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data
Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing… 
Highly Cited
2013
Highly Cited
2013
Introduction to Mathematical Analysis
Preface.- Introduction.- Part 1. A Rigorous Approach to Advanced Calculus.- 1. Preliminaries.- 2. Metric and Topological Spaces I… 
Highly Cited
2011
Highly Cited
2011
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
Appearance modeling is very important for background modeling and object tracking. Subspace learning-based algorithms have been… 
Highly Cited
2010
Highly Cited
2010
Non-Negative Multilinear Principal Component Analysis of Auditory Temporal Modulations for Music Genre Classification
Motivated by psychophysiological investigations on the human auditory system, a bio-inspired two-dimensional auditory… 
Highly Cited
2009
Highly Cited
2009
Bilinear classifiers for visual recognition
We describe an algorithm for learning bilinear SVMs. Bilinear classifiers are a discriminative variant of bilinear models, which… 
Highly Cited
2007
Highly Cited
2007
Robust Visual Tracking Based on Incremental Tensor Subspace Learning
Most existing subspace analysis-based tracking algorithms utilize a flattened vector to represent a target, resulting in a high… 
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… 
2005
2005
Lighting normalization with generic intrinsic illumination subspace for face recognition
In this paper, we introduce the concept of intrinsic illumination subspace which is based on the intrinsic images. This intrinsic… 
Highly Cited
2000
Highly Cited
2000
An incremental learning method for face recognition under continuous video stream
The current technology in computer vision requires humans to collect images, store images, segment images for computers and train… 
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