hh.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Stability analysis of the t-SNE algorithm for human activity pattern data
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0001-9307-9421
JeCom Consulting, Halmstad, Sweden.ORCID-id: 0000-0001-8804-5884
2018 (Engelska)Konferensbidrag, Muntlig presentation med publicerat abstract (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
2018.
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:hh:diva-38442OAI: oai:DiVA.org:hh-38442DiVA, id: diva2:1268394
Konferens
The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018
Projekt
SA3LTillgänglig från: 2018-12-05 Skapad: 2018-12-05 Senast uppdaterad: 2019-01-11Bibliografiskt granskad

Open Access i DiVA

tsne-stability(737 kB)59 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 737 kBChecksumma SHA-512
51a3c04d90a1a03b800194f0f4f7042086fabf54f04eca37b19c145bd6363fccc22a74e0f68c5c5b810cec0853f3aeda3470c4e35d321553b8ebb73d20b32867
Typ fulltextMimetyp application/pdf

Personposter BETA

Ali Hamad, RebeenJärpe, EricLundström, Jens

Sök vidare i DiVA

Av författaren/redaktören
Ali Hamad, RebeenJärpe, EricLundström, Jens
Av organisationen
CAISR Centrum för tillämpade intelligenta system (IS-lab)
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 59 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 325 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf