Optimal combination of feature weight learning and classification based on local approximation

Currently, most feature weights estimation methods are independent on the classification algorithms. The combination of discriminant analysis and classifiers for effective pattern classification remains heuristic. The present study address the topics of learning of feature weights by using a recently reported classification algorithm, K-Local Hyperplane Distance Nearest Neighbor (HKNN) [18], in which the data is modeled as embedded in a linear hyperplane. Motivated by the encouraging performance of the Learning Discriminative Projections and Prototypes, the feature weights are estimated by minimizing the classifier leave-one-out cross validation error of HKNN. Approximated explicit solution is obtained to give feature estimation. Therefore, the feature weighting and classification are perfectly matched. The performance of the combinational model is extensively assessed via experiments on both synthetic and benchmark datasets. The superior results demonstrate that the method is competitive compared with some state-of-art models.