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

Seppo Virtanen; Homayun Afrabandpey; Samuel Kaski

doi:10.1109/ICASSP.2016.7472120

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

Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-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 language	English
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher	IEEE
Pages	2464-2468
Number of pages	5
Volume	2016-May
ISBN (Print)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472120
Publication status	Published - 18 May 2016
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing - Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016 Conference number: 41 http://www.icassp2016.org/

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2016
Country/Territory	China
City	Shanghai
Period	20/03/2016 → 25/03/2016
Internet address	http://www.icassp2016.org/

Keywords

Data visualization
latent factor models
manifold embedding
multi-view learning

Access to Document

10.1109/ICASSP.2016.7472120

Cite this

@inproceedings{730ed97f747d460c905257b66a135c28,

title = "Visualizations relevant to the user by multi-view latent variable factorization",

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.",

keywords = "Data visualization, latent factor models, manifold embedding, multi-view learning",

author = "Seppo Virtanen and Homayun Afrabandpey and Samuel Kaski",

year = "2016",

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doi = "10.1109/ICASSP.2016.7472120",

language = "English",

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booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",

url = "http://www.icassp2016.org/",

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Virtanen, S, Afrabandpey, H & Kaski, S 2016, Visualizations relevant to the user by multi-view latent variable factorization. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 2016-May, 7472120, IEEE, pp. 2464-2468, IEEE International Conference on Acoustics, Speech, and Signal Processing, Shanghai, China, 20/03/2016. https://doi.org/10.1109/ICASSP.2016.7472120

Visualizations relevant to the user by multi-view latent variable factorization. / Virtanen, Seppo; Afrabandpey, Homayun; Kaski, Samuel.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2016-May IEEE, 2016. p. 2464-2468 7472120.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Visualizations relevant to the user by multi-view latent variable factorization

AU - Virtanen, Seppo

AU - Afrabandpey, Homayun

AU - Kaski, Samuel

N1 - Conference code: 41

PY - 2016/5/18

Y1 - 2016/5/18

N2 - 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.

AB - 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.

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KW - latent factor models

KW - manifold embedding

KW - multi-view learning

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ER -

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

Abstract

Conference

Keywords

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Methods for probabilistic modeling of knowledge elicitation for improving machine learning predictions

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Press/Media

Methods for probabilistic modeling of knowledge elicitation for improving machine learning predictions

Cite this