摘要
针对流形学习算法的批量处理方式无法利用已学习的流形结构实现新样本的增量处理问题,提出一种增量式局部切空间排列算法。阐述局部切空间排列算法的基本原理及一次性观测样本全局坐标矩阵迭代和低维坐标提取方法。在算法增量学习上,对因新增样本加入而改变近邻点的样本进行全局坐标更新,建立新样本点的全局坐标;利用原始样本低维嵌入坐标和更新后的全局坐标矩阵对新增样本的低维嵌入坐标进行估计,并采用特征值迭代方法实现全局坐标更新。将增量式局部切空间排列算法应用于滚动轴承四种不同状态的模式识别中,提取的新增样本低维特征与特征空间聚集度分析结果表明:增量式局部切空间排列算法能够在保留一次性观测样本建立的低维流形基础上实现算法的增量式学习,同时对新增样本具有较高的识别率。
In view of the problem of extrapolating the embedding of a manifold learning from the given data points to incremental data points,the incremental local tangent space alignment algorithm is presented.The method of global coordinates matrix iteration and lower-dimensional coordinates computation is introduced.For incremental learning,the global coordinates of affect points are recomputed,and the updated global coordinates matrix is used to construct global coordinate of the new data points.The lower-dimensional embedding coordinates of the incremental points are formulated by the updated global coordinate matrix and lower-dimensional embedding coordinates of the given data points.The lower-dimensional embedding coordinates of all points are updated by the eigenvalue iteration algorithm.Experiments with the proposed method are carried out to identify the four different mode of the vibration signal of rolling bearing.The experiments result of the incremental points embedding and scatter of the different sorts demonstrates that the incremental local tangent space alignment algorithm can extrapolate the given data points manifold to the incremental learning and the incremental data points can embed effectively to the previously learned manifold.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2012年第5期81-86,共6页
Journal of Mechanical Engineering
基金
第二炮兵装备预研基金资助项目(EP090046)
关键词
局部切空间排列算法
增量式学习
模式识别
滚动轴承
Local tangent space alignment algorithm Incremental learning Pattern recognition Rolling bearing
