Feature extraction through local learning
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
|Journal / Publication||Statistical Analysis and Data Mining|
|Publication status||Published - Jul 2009|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-67649974951&origin=recordpage|
RELIEF is considered one of the most successful algorithms for assessing the quality of features. It has been recently proved that RELIEF is an online learning algorithm that solves a convex optimization problem with a margin-based objective function. Starting from this mathematical interpretation, we propose a novel feature extraction algorithm, referred to as local feature extraction (LFE), as a natural generalization of RELIEF. LFE collects discriminant information through local learning and can be solved as an eigenvalue decomposition problem with a closed-form solution. A fast implementation of LFE is derived. Compared to principal component analysis, LFE also has a clear physical meaning and can be implemented easily with a comparable computational cost. Compared to other feature extraction algorithms, LFE has an explicit mechanism to remove irrelevant features. Experiments on synthetic and real-world data are presented. The results demonstrate the effectiveness of the proposed algorithm. © 2009 Wiley Periodicals, Inc.
- Classification, Feature extraction, Local learning, Microarray
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