非负子空间聚类指导下的非负矩阵分解
非负子空间聚类指导下的非负矩阵分解
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海外高层次人才创新创业专项资金项目(KQJSCX20170731165939298)

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Non-Negative Subspace Clustering Guided Non-Negative Matrix Factorization
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    摘要:

    非负矩阵分解作为一种有效的数据表示方法被广泛应用于模式识别和机器学习领域。为了得到原始数据紧致有效的低维数据表示,无监督非负矩阵分解方法在特征降维的过程中通常需要同时发掘数据内部隐含的几何结构信息。通过合理建模数据样本间的相似性关系而构建的相似度图,通常被用来捕获数据样本的空间分布结构信息。子空间聚类可以有效发掘数据内部的子空间结构信息,其获得的自表达系数矩阵可用于构建相似度图。该文提出了一种非负子空间聚类算法来发掘数据的子空间结构信息,同时利用该信息指导非负矩阵分解,从而得到原始数据有效的非负低维表示。同时,该文还提出了一种有效的迭代求解方法来求解非负子空间聚类问题。在两个图像数据集上的聚类实验结果表明,利用数据的子空间结构信息可以有效改善非负矩阵分解的性能。

    Abstract:

    As an effective data representation method, non-negative matrix factorization has been widely used in pattern recognition and machine learning. To obtain a compact and effective data representation in data dimension reduction, unsupervised non-negative matrix factorization usually needs to discover the latent geometry structure information of the data. A similarity graph constructed by reasonably modeling similarity relationships between data samples is typically used to capture spatial distribution structure information for data samples. Subspace learning can effectively explore the subspace structure information inside the data, and the obtained self-expressive coefficient matrix can be used to construct a similarity graph. In this paper, a nonnegative subspace clustering algorithm is proposed to explore the subspace structure information of data which is used to guide the non-negative matrix factorization, so as to obtain the effective non-negative low-dimensional representation of the original data. At the same time, an effective iterative strategy is developed to solve the problem of non-negative subspace clustering. The results of clustering experiments on two image datasets demonstrate that utilizing the subspace structure information of data can effectively improve the performance of non-negative matrix factorization.

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引文格式
崔国盛,李烨.非负子空间聚类指导下的非负矩阵分解 [J].集成技术,2019,8(5):3-12

Citing format
CUI Guosheng, LI Ye. Non-Negative Subspace Clustering Guided Non-Negative Matrix Factorization[J]. Journal of Integration Technology,2019,8(5):3-12

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  • 在线发布日期: 2019-10-09
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