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Visualizations relevant to the user by multi-view latent variable factorization

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Title: Visualizations relevant to the user by multi-view latent variable factorization
Authors: Virtanen, S
Afrabandpey, H
Kaski, S
Item Type: Conference Paper
Abstract: A main goal of data visualization is to find, from among all the available alternatives, mappings to the 2D/3D display which are relevant to the user. Assuming user interaction data, or other auxiliary data about the items or their relationships, the goal is to identify which aspects in the primary data support the user's input and, equally importantly, which aspects of the user's potentially noisy input have support in the primary data. For solving the problem, we introduce a multi-view embedding in which a latent factorization identifies which aspects in the two data views (primary data and user data) are related and which are specific to only one of them. The factorization is a generative model in which the display is parameterized as a part of the factorization and the other factors explain away the aspects not expressible in a two-dimensional display. Functioning of the model is demonstrated on several data sets.
Issue Date: 19-May-2016
Date of Acceptance: 1-Mar-2016
URI: http://hdl.handle.net/10044/1/52913
DOI: https://dx.doi.org/10.1109/ICASSP.2016.7472120
ISSN: 2379-190X
Publisher: IEEE
Start Page: 2464
End Page: 2468
Journal / Book Title: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Conference Name: IEEE International Conference on Acoustics, Speech, and Signal Processing
Keywords: Science & Technology
Technology
Acoustics
Engineering, Electrical & Electronic
Engineering
Data visualization
latent factor models
manifold embedding
multi-view learning
NONLINEAR DIMENSIONALITY REDUCTION
cs.LG
cs.IR
Publication Status: Published
Start Date: 2016-03-20
Finish Date: 2016-03-25
Conference Place: Shanghai, China
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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