CAJ | 학술논문

为了解决局部线性嵌入（locally linear embedding，LLE）流形学习算法无法自适应确定重构区间和不能进行增量学习等问题，提出了一种自适应聚类增量 LLE（clustering adaptively incremental LLE，C-LLE）目标识别算法。该算法通过建立高维非线性样本集的局部线性结构聚类模型，对聚类后的类内样本采用线性重构，解决了 LLE 算法样本重构邻域无法自适应确定的问题；通过构建降维矩阵，解决了 LLE 算法无法单独对增量进行降维和无法利用增量对目标进行识别的问题。实验表明，本文算法能够准确提取高维样本集的低维流形结构，具有较小的增量降维误差和良好的目标识别性能。
위료해결국부선성감입（locally linear embedding，LLE）류형학습산법무법자괄응학정중구구간화불능진행증량학습등문제，제출료일충자괄응취류증량 LLE（clustering adaptively incremental LLE，C-LLE）목표식별산법。해산법통과건립고유비선성양본집적국부선성결구취류모형，대취류후적류내양본채용선성중구，해결료 LLE 산법양본중구린역무법자괄응학정적문제；통과구건강유구진，해결료 LLE 산법무법단독대증량진행강유화무법이용증량대목표진행식별적문제。실험표명，본문산법능구준학제취고유양본집적저유류형결구，구유교소적증량강유오차화량호적목표식별성능。
To solve the problem that incremental learning of locally linear embedding (LLE)cannot get reconfiguration neighborhood adaptively and powerlessly,a target recognition method of clustering adaptively incremental LLE(C-LLE)is proposed.Firstly,the clustering model of the clustering locally linear structure of high-dimensional data is build,so it is able to solve the problem of neighborhood adaptive reconfiguration.Then the proposed algorithm extracts an explicit dimensionality reduction matrix,and the problem of powerlessly in-cremental object recognition is solved.Experimental results show that the proposed algorithm is able to extract the low-dimensional manifold structure of high-dimensional data accurately.It also has low incremental dimen-sion reduction error and great target recognition performance.