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Unsupervised Multiway Data Analysis: A Literature Survey

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2 Author(s)
Acar, E. ; Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY ; Yener, B.

Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.

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Knowledge and Data Engineering, IEEE Transactions on  (Volume:21 ,  Issue: 1 )