多局部约束自表示的谱聚类算法

谱聚类是一种有效的子空间聚类方法被广泛应用于图像聚类、图像分割等领域中。然而,谱聚类方法的性能在一定程度上依赖于图的构建,因此如何构建有效的图成为谱聚类中的关键问题。为了解决现有图构建方法存在的不足,提出一种基于多局部约束的自表示图构建方法。该方法不仅考虑样本的自表示能力,而且考虑样本的局部结构信息。尤为重要的是,在构建局部约束项时,通过自适应加权方式融合多种不同距离度量准则。提出一种迭代优化算法求解目标函数。在三个标准人脸数据库上,验证了该方法的有效性。

Multiple Locality-Constrained Self-Representation for Spectral Clustering

Spectral clustering is an effective subspace clustering method which is widely used in image clustering, image segmentation and others. However, the performance of spectral clustering depends on the construction of graph to a certain extent. Therefore, how to construct an effective graph becomes the key problem in spectral clustering. For the shortcomings of existing graph construction methods, this paper proposes a new method named Multiple Locality Constrained Self Representation (MLCSR) for graph construction. This proposed method considers not only the self-representation ability of the samples, but also the local structure information of the samples. Particularly, it exploits the adaptive weighting to fuse different distance measures when building local constraints. In addition, an iterative optimization algorithm is designed to solve the objective function. Finally, the effectiveness of the proposed method is verified on three standard face databases.