类中心极大的多视角极大熵聚类算法

Multi-view maximum entropy clustering algorithm with center distance maximization

在数据稀少、数据维度高、多视角聚类任务的情况下,传统极大熵聚类算法会因类中心趋于一致,从而导致聚类失败。为解决此类问题,在传统极大熵聚类算法的基础上,引入类中心惩罚机制,融合权重矩阵实现多视角划分融合,构建出类中心极大的多视角极大熵聚类算法。该算法通过调整每个视角上的权重来体现某个视角的重要性,并通过类中心极大惩罚项解决了多视角聚类任务下,因数据稀少、数据维度高导致每个视角上的类中心趋于一致的问题。通过大量实验进一步证明,该算法在处理高维度、数据稀少、存在干扰数据和多视角的数据集时,其聚类效果明显优于传统的聚类算法。

In the face of sparse data,high data dimensions and multi view clustering tasks,the traditional maximum entropy clustering algorithm will cause clustering failure because the class center tends to be consistent.In order to solve this problem,based on the traditional maximum entropy clustering algorithm,this paper introduced the class center punishment mechanism,integrated the weight matrix to achieve multi perspective division and integration,and constructed a multi view maximum entropy clustering algorithm with class center maximum.The algorithm reflected the importance of a certain perspective by adjusting the weight on each perspective,and solved the problem that the class center on each perspective tends to be consistent due to the scarcity of data and high data dimension in the multi perspective clustering task.Through a large number of experiments,it further proves that the clustering effect of this algorithm is significantly better than the traditional clustering algorithm when dealing with high-dimensional,sparse data,interference data participating and multi view data sets.