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Generalized iterative RELIEF for supervised distance metric learning. (English) Zbl 1207.68265
Summary: The RELIEF algorithm is a popular approach for feature weighting. Many extensions of the RELIEF algorithm are developed, and I-RELIEF is one of the famous extensions. In this paper, I-RELIEF is generalized for supervised distance metric learning to yield a Mahananobis distance function. The proposed approach is justified by showing that the objective function of the generalized I-RELIEF is closely related to the expected leave-one-out nearest-neighbor classification rate. In addition, the relationships among the generalized I-RELIEF, the neighbourhood components analysis, and graph embedding are also pointed out. Experimental results on various data sets all demonstrate the superiority of the proposed approach.

MSC:
68T10 Pattern recognition, speech recognition
68T05 Learning and adaptive systems in artificial intelligence
Software:
AR face; UCI-ml
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