Unsupervised Multiway Data Analysis: A Literature Survey

Authors:
Evrim Acar;Bülent Yener
Affiliations:
Rensselaer Polytechnic Institute, Troy;Rensselaer Polytechnic Institute, Troy
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2009

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Abstract

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.