Nonnegative Matrix and Tensor Factorizations [An algorithmic perspective]

作者：Zhou, Guoxu^*; Cichocki, Andrzej; Zhao, Qibin; Xie, Shengli

来源：IEEE Signal Processing Magazine, 2014, 31(3): 54-65.

DOI：10.1109/MSP.2014.2298891

• POLYADIC DECOMPOSITION
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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.

• 出版日期2014-5
• 单位广东工业大学; RIKEN; 上海交通大学