Tensorized Label Learning on Anchor Graph

Graph-based multimedia data clustering has attracted much attention due to the impressive clustering performance for arbitrarily shaped multimedia data. However, existing graph-based clustering methods need post-processing to get labels for multimedia data with high computational complexity. Moreover, it is sub-optimal for label learning due to the fact that they exploit the complementary information embedded in data with different types pixel by pixel. To handle these problems, we present a novel label learning model with good interpretability for clustering. To be specific, our model decomposes anchor graph into the products of two matrices with orthogonal non-negative constraint to directly get soft label without any post-processing, which remarkably reduces the computational complexity. To well exploit the complementary information embedded in multimedia data, we introduce tensor Schatten p-norm regularization on the label tensor which is composed of soft labels of multimedia data. The solution can be obtained by iteratively optimizing four decoupled sub-problems, which can be solved more efficiently with good convergence. Experimental results on various datasets demonstrate the efficiency of our model.

Authors

• Jing Li
• Quanxue Gao Xidian University
• Qianqian Wang Xidian University
• Wei Xia School of Telecommunications Engineering, Xidian University

Keywords

• CV: Learning & Optimization for CV
• CV: Representation Learning for Vision
• ML: Multi-class/Multi-label Learning & Extreme Classification
• ML: Unsupervised & Self-Supervised Learning