半导体光电, 2017, 38 (3): 419, 网络出版: 2017-07-10  

一种融合聚类的监督局部线性嵌入算法研究

Study on Supervised Local Linear Embedding Algorithm Based on Fusion Clustering
作者单位
重庆理工大学 计算机科学与工程系, 重庆 400054
摘要
监督局部线性嵌入算法(SLLE)通过数据点的标签信息进行高维数据在低维特征空间的映射, 针对SLLE在均匀化高维数据的分布和最小化重构代价时, 忽略类内偏离总体分布的稀疏离散数据在线性重构过程中可能错误地投影在其他超平面的情形, 引入Kmeans++算法调整样本间距离, 进行最优近邻点的选择, 从而更有效地反映数据在高维空间中的实际分布, 使降维后的数据具备更好的可分性。通过ORL以及Yale人脸数据集上的仿真实验, 结果显示, 该方法具有更强的泛化能力及更高的识别率。
Abstract
The supervised local linear embedding algorithm (SLLE) maps the high dimensional data in the low dimensional feature space through the label information of the data points. In the process of homogenizing the high dimensional data distribution and minimizing the reconstruction cost and for the situation that the sparse discrete data ignored in-class deviations from the population distribution may be incorrectly projected in other hyperplanes during the linear reconstruction, the Kmeans ++ algorithm is introduced to adjust the distance between the samples, and the selection of the optimal neighbor points making the data more efficiently reflect the actual distribution in the high-dimensional space, so that the reduced dimension of the data has better separability. Through the simulation of ORL and Yale data set, the proposed method has stronger generalization ability and higher recognition rate.

王东, 张强, 严亮. 一种融合聚类的监督局部线性嵌入算法研究[J]. 半导体光电, 2017, 38(3): 419. WANG Dong, ZHANG Qiang, YAN Liang. Study on Supervised Local Linear Embedding Algorithm Based on Fusion Clustering[J]. Semiconductor Optoelectronics, 2017, 38(3): 419.

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