hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Stability analysis of the t-SNE algorithm for human activity pattern data
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-9307-9421
JeCom Consulting, Halmstad, Sweden.ORCID iD: 0000-0001-8804-5884
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

Place, publisher, year, edition, pages
2018.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-38442OAI: oai:DiVA.org:hh-38442DiVA, id: diva2:1268394
Conference
The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018
Projects
SA3LAvailable from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-01-11Bibliographically approved

Open Access in DiVA

tsne-stability(737 kB)45 downloads
File information
File name FULLTEXT01.pdfFile size 737 kBChecksum SHA-512
51a3c04d90a1a03b800194f0f4f7042086fabf54f04eca37b19c145bd6363fccc22a74e0f68c5c5b810cec0853f3aeda3470c4e35d321553b8ebb73d20b32867
Type fulltextMimetype application/pdf

Authority records BETA

Ali Hamad, RebeenJärpe, EricLundström, Jens

Search in DiVA

By author/editor
Ali Hamad, RebeenJärpe, EricLundström, Jens
By organisation
CAISR - Center for Applied Intelligent Systems Research
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 45 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 212 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf