基于压缩传感的邻域嵌入

Compressive Sensing Based Neighborhood Embedding

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.