Learning of Nonnegative Matrix Factorization Models for Inconsistent Resolution Dataset Analysis

Masahiro KOHJIMA  Tatsushi MATSUBAYASHI  Hiroshi SAWADA  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.4   pp.715-723
Publication Date: 2019/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018AWI0002
Type of Manuscript: INVITED PAPER (Special Section on Award-winning Papers)
inconsistent resolution dataset,  probabilistic model,  nonnegative matrix factorization,  collective matrix factorization,  

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Due to the need to protect personal information and the impracticality of exhaustive data collection, there is increasing need to deal with datasets with various levels of granularity, such as user-individual data and user-group data. In this study, we propose a new method for jointly analyzing multiple datasets with different granularity. The proposed method is a probabilistic model based on nonnegative matrix factorization, which is derived by introducing latent variables that indicate the high-resolution data underlying the low-resolution data. Experiments on purchase logs show that the proposed method has a better performance than the existing methods. Furthermore, by deriving an extension of the proposed method, we show that the proposed method is a new fundamental approach for analyzing datasets with different granularity.