A survey of multilinear subspace learning for tensor data

By Haiping Lu, K. Plataniotis, Anastasios N. Venetsanopoulos. Pattern Recognition, 2011

380 citations recorded by the source corpus.

Subfields: Computational Mathematics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b1b9aa047dbfb219588b26705dc23ddc44808d78

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In Computational Mathematics

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Domain Adaptation by Mixture of Alignments of Second-or Higher-Order Scatter Tensors (2016, background): “A survey of multi-linear algebraic methods for tensor subspace learning is available in [29].”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, methodology): “We have shown that Tucker decomposition may be considered as a multilinear extension of PCA [8]; it therefore generalizes signal subspace techniques, with applications including classification, feature extraction, and subspace-based harmonic retrieval [25], [39], [75], [76].”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, neutral): “Review and tutorial papers (Kolda and Bader, 2009; Lu et al. , 2011; Grasedyck et al. , 2013; Cichocki et al. , 2015b; de Almeida et al. , 2015; Sidiropoulos et al. , 2016; Papalexakis et al. , 2016; Bachmayr et al. , 2016) and books (Smilde et al. , 2004; Kroonenberg, 2008; Cichocki et al. , 2009;…”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, methodology): “…(cid:4) a ð N Þ , produces a N th order tensor X where each element Tensor factorization or decomposition is a natural extension of matrix factorization and utilizes information from the multiway structure that is lost when modes are collapsed to use matrix factorization algorithms [48,49,51,52].”
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (2016, background): “A survey of multi-linear algebraic methods for tensor subspace learning and applications is available in [30].”
Tensor Decomposition for Signal Processing and Machine Learning (2016, background): “See [133], [134] for more on the topic of multilinear subspace learning.”
Multilinear Discriminant Analysis for Higher-Order Tensor Data Classification (2014, methodology): “Index Terms—Linear discriminant analysis (LDA), multilinear discriminant analysis (MDA), direct general tensor discriminant analysis (DGTDA), constrained multilinear discriminant analysis (CMDA), pattern classification, higher-order tensor Ç”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, methodology): “There are a large number of methods to reduce dimension [147,109,56].”
Tensors for Data Mining and Data Fusion (2016, neutral): “Subsequently, more survey papers have been published, some of them focusing on the applications [Acar and Yener 2009] and some on the algorithms [Cichocki et al. 2009; Lu et al. 2011; Grasedyck et al. 2013; Cichocki et al. 2015].”