Generalized iterative RELIEF for supervised distance metric learning
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. (C) 2010 Elsevier Ltd. All rights reserved.
iterative relief distance metric learning feature weighting