The large-scale multi-view clustering algorithms, based on the anchor graph,
have shown promising performance and efficiency and have been extensively
explored in recent years. Despite their successes, current methods lack
interpretability in the clustering process and do not sufficiently consider the
complementary information across different views. To address these
shortcomings, we introduce the One-Step Multi-View Clustering Based on
Transition Probability (OSMVC-TP). This method adopts a probabilistic approach,
which leverages the anchor graph, representing the transition probabilities
from samples to anchor points. Our method directly learns the transition
probabilities from anchor points to categories, and calculates the transition
probabilities from samples to categories, thus obtaining soft label matrices
for samples and anchor points, enhancing the interpretability of clustering.
Furthermore, to maintain consistency in labels across different views, we apply
a Schatten p-norm constraint on the tensor composed of the soft labels. This
approach effectively harnesses the complementary information among the views.
Extensive experiments have confirmed the effectiveness and robustness of
OSMVC-TP.

基于过渡概率的一步多视角聚类方法（OSMVC-TP）为大规模多视角聚类算法引入了概率方法，利用锚图从样本到锚点的过渡概率来直接学习锚点到类别的过渡概率和样本到类别的过渡概率，从而获得样本和锚点的软标签矩阵，增强了聚类的可解释性，并通过在软标签的张量上应用 Schatten p - 范数约束，保持了不同视角间标签的一致性，进一步有效利用了视角间的互补信息。广泛的实验已经证实了 OSMVC-TP 的有效性和稳健性。