论文部分内容阅读
Spectral clustering with pairwise constraints(i.e. mustlink and cannotlink) has been a hot topic in the machine learning community in recent years. Its performances are significantly influenced by utilizing the constraints. To make full use of the constraints’ effect, pairwise constraints are integrated into an affinity matrix based on the gravitational method. In the data set as input, each point has mass property, and interacts with each other according to the universal law of gravitation. A Gravitational inspired constrained spectral clustering(GCSC) algorithm is proposed in this paper. Our algorithm is evaluated on multiple benchmark classification datasets. Compared with the existing approaches, experimental results demonstrate the effectiveness of our presented algorithm.
Spectral clustering with pairwise constraints (ie must link and cannotlink) has been a hot topic in the machine learning community in recent years. It’s full of use of the constraints’ effect, pairwise constraints are integrated into an affinity matrix based on the gravitational method. In the data set as input, each point has mass property, and interacts with each other according to the universal law of gravitation. A Gravitational-inspired constrained spectral clustering (GCSC) algorithm is proposed in this paper Compared with the existing approaches, experimental results demonstrate the effectiveness of our presented algorithm.