关键词: k最近邻, 噪声样本, 降维, 分类器, 流形

Abstract: For resolving the problem of the existing noise sample and large number of dimensions, the k-nearest neighbors classifier over manifolds was presented in this paper. Firstly the classic k-nearest neighbors was extended by Bayes theorem and local joint probability density was estimated by kernel density estimation in classifier. In addition, after building the noise sample model, an objective function was defined via improved marginal intrinsic graph and its weight matrix for searching the optimal dimension reduction mapping matrix. At last, details about k-nearest neighbors algorithm over manifolds were provided. The experimental results demonstrate that the presented method has lower classification error rate than six kinds of classic methods in most cases on twelve data sets.

Key words: k-Nearest Neighbors, noise sample, dimensionality reduction, classifier, manifold