基于子空间差异的投影聚类算法

针对传统K-means型软子空间聚类技术中子空间差异度量定义的困难问题，提出一种基于概率距离的子空间差异表示模型，以此为基础提出一种自适应的投影聚类算法。该方法首先基于子空间聚类理论提出一个描述各簇类所关联的软子空间之间的相异度公式；其次，将其与软子空间聚类相结合，定义了聚类目标优化函数，并根据局部搜索策略给出了聚类算法过程。在合成和实际数据集上进行了一系列实验，结果表明该算法引入子空间比较可以为簇类学习更优的软子空间；与现有主流子空间聚类算法相比，所提算法大幅度提升了聚类精度，适用于高维数据聚类分析。

Projective clustering algorithm based on subspace difference

Aiming at the challenge of defining the subspace dissimilarity in traditional K-means soft subspace clustering techniques, this paper proposed a novel probability distance-based subspace difference representation model as the basis for an adaptive projection clustering algorithm. Firstly, based on the subspace clustering theory, the proposed method formulated a formula to describe the dissimilarities between associated soft subspaces. Secondly, by combining this formula with soft subspace clustering, it defined a clustering objective optimization function and provided a detailed clustering algorithm process according to a local search strategy. A series of experiments on both synthetic and real-world datasets demonstrate that the introduction of subspace comparison can lead to learning a more optimal soft subspace for clusters. Compared to existing mainstream subspace clustering algorithms, the proposed algorithm significantly improves the clustering accuracy, making it suitable for high-dimensional data clustering analysis.