liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes
School of Engineering and Information Technology, The University of New South Wales, ADFA, Canberra, ACT 2600, Australia .
School of Engineering and Information Technology, The University of New South Wales, ADFA, Canberra, ACT 2600, Australia.ORCID-id: 0000-0002-4255-5130
Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA .
2010 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Resurstyp
Text
Abstract [en]

With the fast development of multi‐dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering‐based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype‐image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ‐HMM) and a well‐known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG‐HMM) will be carried out. The experimental results show that the performances of both FDVQ‐HMM and LBG‐HMM are almost similar. Finally, we have justified the competitiveness of FDVQ‐HMM in classification of cellular phenotype image database by using hypotheses t‐test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome‐wide screening image data.

Ort, förlag, år, upplaga, sidor
2010. Vol. 1210, s. 119-130
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:liu:diva-127892DOI: 10.1063/1.3314264 OAI: oai:DiVA.org:liu-127892DiVA, id: diva2:928908
Konferens
2009 INTERNATIONAL CONFERNECE ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-09). 28–29 July 2009,Sofia (Bulgaria)
Tillgänglig från: 2016-05-17 Skapad: 2016-05-13 Senast uppdaterad: 2018-01-10

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Personposter BETA

Pham, Tuan D

Sök vidare i DiVA

Av författaren/redaktören
Pham, Tuan D
Datorseende och robotik (autonoma system)

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 79 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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