IEEE Signal Processing Magazine· 2014Q1
Tensors : A brief introduction
- 346citations
- Q1SCImago
- 2014year
Short summary
Tensors, particularly the canonical polyadic (CP) decomposition, are fundamental to blind source separation (BSS) algorithms, enabling the identification of underdetermined mixtures.
AI-generated from the title and abstract; the full text is not read.
Key points
- Tensor decompositions are core to many blind source separation (BSS) algorithms.
- Canonical polyadic (CP) tensor decomposition is key for identifying underdetermined mixtures.
- Tensors and matrices possess distinct properties, differentiating CP decomposition from SVD.
- The paper introduces the fundamental differences and properties of tensors versus matrices.
AI-generated from the title and abstract; the full text is not read.
Abstract
Tensor decompositions are at the core of many blind source separation (BSS) algorithms, either explicitly or implicitly. In particular, the canonical polyadic (CP) tensor decomposition plays a central role in the identification of underdetermined mixtures. Despite some similarities, CP and singular value decomposition (SVD) are quite different. More generally, tensors and matrices enjoy different properties, as pointed out in this brief introduction.
The authors' abstract, as published at the source. IEEE Signal Processing Magazine, 2014 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics