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 e criterion. Motivated by compressive sensing theory, the strategy of neighborhood construction is proposed based on the linear combination of l2 and l1, 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.%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.
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