Locally linear embedding(LLE)algorithm has a distinct deficiency in practical application.It requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is...Locally linear embedding(LLE)algorithm has a distinct deficiency in practical application.It requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select k.Different samples will produce different k adaptively according to the density of the data distribution.The method is applied to classify plant leaves.The experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.展开更多
基金This study was financially supported by the National Natural Science Foundation of China(61172127)the Research Fund for the Doctoral Program of Higher Education(KJQN1114)+2 种基金Anhui Provincial Natural Science Foundation(1308085QC58)the 211 Project Youth Scientific Research Fund of Anhui UniversityProvincial Natural Science Foundation of Anhui Universities(KJ2013A026)。
文摘Locally linear embedding(LLE)algorithm has a distinct deficiency in practical application.It requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select k.Different samples will produce different k adaptively according to the density of the data distribution.The method is applied to classify plant leaves.The experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.
