近邻法是模式识别中的经典算法之一,其分类性能高度依赖样本间的距离度量方式.适当的距离度量方式有助于提高近邻法的分类性能.然而,当前此类算法多从判别模型的角度寻找最大化分类效果的度量,忽略了各类样本集的类聚集属性.鉴于此,基于最小成分本征向量提出一种子空间投影近邻分类算法(nearest neighbor classification algorithm based on minimum component eigenvector subspace projection,NN_MCESP).该算法结合了经典的主成分分析和近邻法,能够有效地实现基于最小成分本征向量投影的各类样本聚集属性分析,并完成基于子空间近邻投票准则的分类.在多组分类数据集上通过与其他分类算法的实验对比,验证了 NN_MCESP算法的有效性和稳定性.
Nearest Neighbor Classification Algorithm Based on Minimum Component Eigenvector Subspace Projection
The nearest neighbor algorithm is one of the most classical pattern recognition algorithms,which classification performance highly depends on the distance metric between samples.Appropriate distance metric can help improve the classification performance of the algorithm.However,such algorithms mostly seek metrics to maximize classification effectiveness from the perspective of discriminant models currently,ignoring the aggregation properties of various sample sets belonging to different classes.In view of this,a nearest neighbor classification algorithm based on minimum component eigenvector subspace projection(NN_MCESP)was proposed.This algorithm combined classic principal component analysis(PC A)and nearest neighbor algorithm,which can effectively implement aggregation properties analysis of various sample clusters based on minimum component eigenvector projection,and complete classification based on subspace nearest neighbor voting criteria.The effectiveness and stability of NN_MCESP were validated by comparing with other classification algorithms on multiple data sets.
万方数据
维普
