Confident identification of relevant objects based on nonlinear rescaling method and transductive inference

Shen Shyang Ho, Roman Polyak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel machine learning algorithm to identify relevant objects from a large amount of data. This approach is driven by linear discrimination based on Nonlinear Rescaling (NR) method and transductive inference. The NR algorithm for linear discrimination (NRLD) computes both the primal and the dual approximation at each step. The dual variables associated with the given labeled data-set provide important information about the objects in the data-set and play the key role in ordering these objects. A confidence score based on a transductive inference procedure using NRLD is used to rank and identify the relevant objects from a pool of unlabeled data. Experimental results on an unbalanced protein data-set for the drug target prioritization and identification problem are used to illustrate the feasibility of the proposed identification algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages505-510
Number of pages6
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other7th IEEE International Conference on Data Mining, ICDM 2007
Country/TerritoryUnited States
CityOmaha, NE
Period10/28/0710/31/07

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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