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

Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherIEEE
Pages2464-2468
Number of pages5
Volume2016-May
ISBN (Print)9781479999880
DOIs
Publication statusPublished - 18 May 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016
Conference number: 41
http://www.icassp2016.org/

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2016
CountryChina
CityShanghai
Period20/03/201625/03/2016
Internet address

Keywords

  • Data visualization
  • latent factor models
  • manifold embedding
  • multi-view learning

Fingerprint Dive into the research topics of 'Visualizations relevant to the user by multi-view latent variable factorization'. Together they form a unique fingerprint.

Cite this