基于局部平滑性的通用增量流形学习算法  

Generalized incremental manifold learning algorithm based on local smoothness

作　　者：周雪燕[1] 韩建敏[2] 詹宇斌[3] 

机构地区：[1]中原工学院计算机学院,郑州450000 [2]河南经贸职业学院信息管理系,郑州450000 [3]国防科学技术大学计算机学院,长沙410073

出　　处：《计算机应用》2012年第6期1670-1673,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61179040;60970034);河南省科技攻关计划项目(102102210509)

摘　　要：目前大多数流形学习算法无法获取高维输入空间到低维嵌入空间的映射,无法处理新增数据,因此无增量学习能力。而已有的增量流形学习算法大多是通过扩展某一特定的流形学习算法使其具备增量学习能力,不具有通用性。针对这一问题,提出了一种通用的增量流形学习(GIML)算法。该方法充分考虑流形的局部平滑性这一本质特征,利用局部主成分分析法来提取数据集的局部平滑结构,并寻找包含新增样本点的局部平滑结构到对应训练数据的低维嵌入坐标的最佳变换。最后GIML算法利用该变换计算新增样本点的低维嵌入坐标。在人工数据集和实际图像数据集上进行了系统而广泛的比较实验,实验结果表明GIML算法是一种高效通用的增量流形学习方法,且相比当前主要的增量算法,能更精确地获取增量数据的低维嵌入坐标。Most of the existing manifold learning algorithms are not capable of dealing with new arrival samples.Although some incremental algorithms are developed via extending a specified manifold learning algorithm,most of them have some disadvantages more or less.In this paper,a new and more Generalized Incremental Manifold Learning（GIML） algorithm based on local smoothness was proposed.GIML algorithm firstly extracted the local smooth structure of data set via local Principal Component Analysis（PCA）.Then the optimal linear transformation,which transformed the local smooth structure of new arrival sample＇s neighborhood to its correspondent low-dimensional embedding coordinates,was computed.Finally the low-dimensional embedding coordinates of new arrival samples were obtained by the optimal transformation.Extensive and systematic experiments were conducted on both artificial and real image data sets.The experimental results demonstrate that the GIML algorithm is an effective incremental manifold learning algorithm and outperforms other existing algorithms.

关 键 词：维数归约 流形学习 增量学习 局部切空间对齐 局部线性嵌入 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]