基于压缩传感的邻域嵌入  

Compressive Sensing Based Neighborhood Embedding

作　　者：贾泂[1] 郑忠龙[1] 杨杰[2] 

机构地区：[1]浙江师范大学计算机科学与技术系,金华321004 [2]上海交通大学图像处理与模式识别研究所,上海200240

出　　处：《模式识别与人工智能》2012年第4期684-690,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60805001;61170109);国家863计划项目(No.2007AA01Z164);浙江省自然科学基金项目(No.Y1100161;Y1090579);浙江省科技厅项目(No.2012C21021)资助

摘　　要：基于谱流形学习算法的一个核心问题是局部邻域的构建,可通过KNN或ε准则构建局部邻域.受压缩传感理论的启发,提出一种基于l2和l1范数重构准则的邻域构建模式,称之为基于压缩传感的邻域嵌入(CSNE).在此基础上,利用无标签数据,提出半监督的CSNE.在多个数据集上的可视化和半监督分类实验,证明该算法的有效性.How to construct local neighborhoods is one of the key points of spectral-manifold based algorithms. For example, locally linear embedding （LLE）, one of the traditional manifold learning algorithms, constructs the local relationships through KNN or s criterion. Motivated by compressive sensing theory, the strategy of neighborhood construction is proposed based on the linear combination of/2 and 11 , which is called compressive sensing based neighborhood embedding （CSNE）. The proposed strategy can not only be applied to LLE, but also to other spectral learning methods while neighborhoods need to be constructed. In addition, the semi-supervised CSNE algorithm is presented while the un-labeled data are taken into account. The results of visualization and classification experiments on several datasets demonstrates the competitive results of the proposed algorithm compared with PCA, LDA, LPP and S-Isomap.

关 键 词：流形学习 压缩传感(CS) 半监督学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]