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Ordinal mixed membership models

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Title: Ordinal mixed membership models
Authors: Virtanen, S
Girolami, M
Item Type: Conference Paper
Abstract: We present a novel class of mixed membership models for joint distributions of groups of observations that co-occur with ordinal response variables for each group for learning statistical associations between the ordinal response variables and the observation groups. The class of proposed models addresses a requirement for predictive and diagnostic methods in a wide range of practical contemporary applications. In this work, by way of illustration, we apply the models to a collection of consumer-generated reviews of mobile software applications, where each review contains unstructured text data accompanied with an ordinal rating, and demonstrate that the models infer useful and meaningful recurring patterns of consumer feedback. We also compare the developed models to relevant existing works, which rely on improper statistical assumptions for ordinal variables, showing significant improvements both in predictive ability and knowledge extraction.
Issue Date: 1-Jun-2015
Date of Acceptance: 1-Jun-2015
URI: http://hdl.handle.net/10044/1/67106
ISBN: 9781510810587
Publisher: Proceedings of Machine Learning Research (PMLR)
Start Page: 588
End Page: 596
Journal / Book Title: Proceedings of Machine Learning Research
Volume: 37
Copyright Statement: © 2015 The Author(s). This paper is licensed under the terms of the Creative Commons Attribution 4.0 International License, which is incorporated herein by reference and is further specified at http://creativecommons.org/licenses/by/4.0/legalcode (human readable summary at http://creativecommons.org/licenses/by/4.0).
Conference Name: International Conference on Machine Learning
Publication Status: Published
Start Date: 2015-07-07
Finish Date: 2015-07-09
Conference Place: Lille, France
Appears in Collections:Mathematics
Faculty of Natural Sciences