基于TL1范数约束的子空间聚类方法

李海洋; 王恒远

doi:10.11999/JEIT170193

基于TL1范数约束的子空间聚类方法

doi: 10.11999/JEIT170193

李海洋¹,
王恒远¹

基金项目:

国家自然科学基金(11271297)，陕西省自然科学基金(2015JM1020)

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- 文章访问数: 1147
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- 被引次数: 0
出版历程
- 收稿日期: 2017-03-03
- 修回日期: 2017-06-27
- 刊出日期: 2017-10-19

Subspace Clustering Method Based on TL1 Norm Constraints

LI Haiyang¹,
WANG Hengyuan¹

Funds:

The National Natural Science Foundation of China (11271297), The Natural Science Foundation of Shaanxi Province (2015JM1020)

摘要

摘要: 该文将TL1范数应用于子空间聚类的研究中，提出基于TL1范数约束的子空间聚类优化模型。尽管该优化模型是非凸的，在无噪音的情形下，证明了它的最优解为具有块对角结构的系数矩阵，这对随后进行的谱聚类提供了理论保证；在有噪声的情形下，它的约束条件等价于以干净数据为字典的优化模型，因而求解出的系数矩阵提高了聚类的精确度。进一步，利用增广拉格朗日-交替方向乘子方法给出该优化模型的求解方法。实验结果表明，基于TL1范数的子空间聚类方法不仅增强了系数矩阵的稀疏性，而且在聚类精确度，对噪音的鲁棒性方面要优于低秩子空间聚类方法和稀疏子空间聚类方法。
- TL1范数 /
- 子空间聚类方法 /
- 稀疏 /
- 低秩 /
- 谱聚类
Abstract: The TL1 norm is applied to propose a new optimization model for the study of subspace clustering. Although the optimization is nonconvex, in the case of non-noise, it proves that the optimal solution of the proposed model is the coefficient matrix with block-diagonal structure, which provides the theoretical guarantee for the latter spectral clustering. In the case of dealing with noise, the constraint condition of this model is presented to be equivalent with the optimal model using the corrected data as the dictionary, which contributes to improving the clustering accuracy. Then, the alternating direction method of Lagrangian multipliers is applied to solving the unknown matrices. Experimental results show that subspace clustering method based on TL1 norm not only enhances the sparsity of coefficient matrix, but also is superior to low-rank subspace clustering and sparse subspace clustering method in terms of clustering accuracy and robustness to noise.
- TL1 norm /
- Subspace clustering method /
- Sparse /
- Low-rank /
- Spectral clustering

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参考文献(33)

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