One-Step Multi-View Clustering Based on Transition Probability
Wenhui Zhao Quanxue Gao Guangfei Li Cheng Deng Ming Yang
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.