Efficient information‐theoretic unsupervised feature selection. Issue 2 (1st January 2018)
- Record Type:
- Journal Article
- Title:
- Efficient information‐theoretic unsupervised feature selection. Issue 2 (1st January 2018)
- Main Title:
- Efficient information‐theoretic unsupervised feature selection
- Authors:
- Lee, J.
Seo, W.
Kim, D.‐W. - Abstract:
- Abstract : The method proposed in this Letter selects a feature subset that preserves the data quality in the viewpoint of information theory. Using an efficient information‐theoretic evaluation, the proposed method identifies the final feature subset significantly faster than conventional methods.
- Is Part Of:
- Electronics letters. Volume 54:Issue 2(2018)
- Journal:
- Electronics letters
- Issue:
- Volume 54:Issue 2(2018)
- Issue Display:
- Volume 54, Issue 2 (2018)
- Year:
- 2018
- Volume:
- 54
- Issue:
- 2
- Issue Sort Value:
- 2018-0054-0002-0000
- Page Start:
- 76
- Page End:
- 77
- Publication Date:
- 2018-01-01
- Subjects:
- unsupervised learning -- feature selection
information‐theoretic unsupervised feature selection
Electronics -- Periodicals
621.381 - Journal URLs:
- http://digital-library.theiet.org/content/journals/el ↗
http://estar.bl.uk/cgi-bin/sciserv.pl?collection=journals&journal=00135194 ↗
https://ietresearch.onlinelibrary.wiley.com/loi/1350911x ↗
http://www.theiet.org/ ↗ - DOI:
- 10.1049/el.2017.2476 ↗
- Languages:
- English
- ISSNs:
- 0013-5194
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3705.060000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16455.xml