Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective

Abstract

A common thread in various approaches for model reduction, clustering, feature extraction, classification, and blind source separation (BSS) is to represent the original data by a lower-dimensional approximation obtained via matrix or tensor (multiway array) factorizations or decompositions. The notion of matrix/tensor factorizations arises in a wide range of important applications and each matrix/tensor factorization makes different assumptions regarding component (factor) matrices and their underlying structures. So choosing the appropriate one is critical in each application domain. Approximate low-rank matrix and tensor factorizations play fundamental roles in enhancing the data and extracting latent (hidden) components.

DOI: 10.1109/MSP.2014.2298891

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Cite this paper

@article{Zhou2014NonnegativeMA, title={Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective}, author={Guoxu Zhou and Andrzej Cichocki and Qibin Zhao and Shengli Xie}, journal={IEEE Signal Processing Magazine}, year={2014}, volume={31}, pages={54-65} }