Tensors : A brief introduction

By Pierre Comon. IEEE Signal Processing Magazine, 2014

345 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1c547d673289e0f85844715341fed107f294b5c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. N. Sidiropoulos
2. Evangelos E. Papalexakis
3. Christos Faloutsos
4. Andrzej S. Cichocki
5. Anh Huy Phan
6. Christian Jutten
7. T. Adalı
8. Dana Lahat

In Computational Mathematics

1. Andrzej S. Cichocki
2. N. Sidiropoulos
3. Anh Huy Phan
4. Evangelos E. Papalexakis
5. Christos Faloutsos
6. Qibin Zhao
7. Danilo P. Mandic
8. Lieven De Lathauwer

In Signal Processing

1. Andrzej S. Cichocki
2. Anh Huy Phan
3. Lieven De Lathauwer
4. Qibin Zhao
5. N. Sidiropoulos
6. Danilo P. Mandic
7. Evangelos E. Papalexakis
8. Christos Faloutsos

Citations in the corpus

• Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, background): “This boils down to matrices representing linear transformations and quadratic forms, while tensors are connected with multilinear mappings and multivariate polynomials [31].”
• Tensor decomposition of EEG signals: a brief review. (2015, neutral): “The existing key reviews for tensor decomposition often include its history, models, algorithms and various applications (Acar & Yener, 2009; Cichocki et al., 2015; Comon, 2014; Comon, Luciani, & De Almeida, 2009; Khoromskij, 2011; Kolda & Bader, 2009; Lu, Plataniotis, & Venetsanopoulos, 2011; Morup”
• Tensor Decomposition for Signal Processing and Machine Learning (2016, background): “X has rank equal to 3, but border rank equal to 2 [15].”
• Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, neutral): “For recent surveys and more detailed information we refer to (Kolda and Bader, 2009; Grasedyck et al., 2013; Comon, 2014; Cichocki et al., 2015b; Sidiropoulos et al., 2016).”
• Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “A second generalization is by combining several types of constraints and assumptions: for example, PARAFAC may be combined with statistical independence [104], [138], nonnegativity, sparsity, as well as structure of the latent factors: Vandermonde [72]–[75], Toeplitz [76], among others [16], [65], [”