| 基于压缩传感的邻域嵌入 |
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| 引用本文： | 贾泂,郑忠龙,杨杰.基于压缩传感的邻域嵌入[J].模式识别与人工智能,2012,25(4):684-690. |
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| 作者姓名： | 贾泂 郑忠龙 杨杰 |
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| 作者单位： | 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 |
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| 基金项目： | 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 |
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| 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.
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| 关 键 词： | 流形学习 压缩传感(CS) 半监督学习 |
Compressive Sensing Based Neighborhood Embedding |
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| JIA Jiong
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ZHENG Zhong-Long
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YANG Jie.Compressive Sensing Based Neighborhood Embedding[J].Pattern Recognition and Artificial Intelligence,2012,25(4):684-690. |
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| Authors: | JIA Jiong ZHENG Zhong-Long YANG Jie |
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| Affiliation: | 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) |
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| 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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| Keywords: | Manifold Learning Compressive Sensing (CS) Semi-Supervised Learning |
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