Adaptive Multi-view-fusion Clustering Algorithm Based on Self-correlative Matrix

In multi-view clustering problems, the complementary information and difference information between views have impact on the clustering effect, while the importance conveyed by the sample points also affects the clustering effect. Some existing methods do not fully utilize the complementary information between views, some do not consider the difference information between views, and some do not utilize the importance of sample points, resulting in poor clustering performance. To address the above issues, an adaptive multi-view-fusion clustering algorithm based on self-correlative matrix (AMCSM) is proposed. Firstly, feature concatenating is used to better utilize complementary information between views; Secondly, the auto-weighted mechanism is introduced to adaptively assign appropriate weights to each view, to fully utilize the difference information between views; Finally, diagonal weighted matrices and self-correlative matrices are simultaneously utilized to mine important information conveyed by the sample points. A unified multi-step iterative framework is designed to integrate the above optimization solutions, so that complementary information, difference information, and important information of sample points can promote and learn from each other during the iteration process. The experimental results show that the proposed algorithm achieves excellent results in evaluation metrics such as sensitivity, precision, specificity, adjusted Rand Index, and Matthews correlation coefficient, which is more robust.