NDLI: Interpretable magnification factors for topographic maps of high dimensional and structured data

Content Provider	IEEE Xplore Digital Library
Author	Gianniotis, N.
Copyright Year	2013
Description	Author affiliation: Inst. of Earth & Environ. Sci., Univ. of Potsdam, Potsdam, Germany (Gianniotis, N.)
Abstract	Visualisation of high-dimensional data is typically formulated as a non-linear mapping between a high-dimensional space and a two-dimensional latent space. The goal is that similar data items should be projected to similar coordinates in the latent space. Nevertheless, due to the non-linearity data items that are distant in the high-dimensional space may be projected close to each other in the latent space. Therefore, magnification factors must be analysed in order to detect stretches and contractions in the embedded latent space. However, magnification factors may not be straightforward to communicate to practitioners who are not aware of such manifestations in the embedded latent space, and only care about an accurate depiction that shows which data items are similar. The goal of this work is to devise a more convenient visualisation that corrects for magnifications and thus depicts the true distances between data items more faithfully. We present an approach based on a multidimensional scaling technique that corrects the obtained visualisations by distorting them according to local magnification factors. The approach is general and we demonstrate it on different visualisation algorithms, such as GTM extensions, the autoencoder, the GP-LVM, and on different types of data.
Starting Page	238
Ending Page	245
File Size	697275
Page Count	8
File Format	PDF
ISBN	9781467358958
DOI	10.1109/CIDM.2013.6597242
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Visualization Data visualization Hidden Markov models Boats Probabilistic logic Data models
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Probabilistic Self-Organizing Maps for multivariate sequences

Visualization of Tree-Structured Data Through Generative Topographic Mapping

Compositional Generative Mapping for Tree-Structured Data—Part II: Topographic Projection Model

Visualizing states in autoregressive hidden Markov models using generative topographic mapping

Behavior Analysis Modulus for harbor security

Multi-agent event recognition in structured scenarios

Compositional generative mapping of structured data

Probabilistic dance performance alignment by fusion of multimodal features

Data monitoring of spacecraft using mixture probabilistic principal component analysis and hidden Semi-Markov models

Interpretable magnification factors for topographic maps of high dimensional and structured data

Similar Documents

Probabilistic Self-Organizing Maps for multivariate sequences

Visualization of Tree-Structured Data Through Generative Topographic Mapping

Compositional Generative Mapping for Tree-Structured Data—Part II: Topographic Projection Model

Visualizing states in autoregressive hidden Markov models using generative topographic mapping

Behavior Analysis Modulus for harbor security

Multi-agent event recognition in structured scenarios

Compositional generative mapping of structured data

Probabilistic dance performance alignment by fusion of multimodal features

Data monitoring of spacecraft using mixture probabilistic principal component analysis and hidden Semi-Markov models

Interpretable magnification factors for topographic maps of high dimensional and structured data