Robust relief-feature weighting, margin maximization, and fuzzy optimization
IEEE Transactions on Fuzzy Systems
Kernel-based feature extraction under maximum margin criterion
Journal of Visual Communication and Image Representation
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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. Statistical Analysis and Data Mining 2: 34-47, 2009