1.Department of Computer Science and Technology,Zhejiang Normal University,Jinhua 321004 2.Institute of Image Processing and Pattern Recognition,Shanghai Jiaotong University,Shanghai 200240
Abstract 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 ε 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.
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2012, Vol. 25 
