Tensorized Label Learning on Anchor Graph
DOI:
https://doi.org/10.1609/aaai.v38i12.29257Keywords:
ML: Unsupervised & Self-Supervised Learning, CV: Learning & Optimization for CV, CV: Representation Learning for Vision, ML: Multi-class/Multi-label Learning & Extreme ClassificationAbstract
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.Downloads
Published
2024-03-24
How to Cite
Li, J., Gao, Q., Wang, Q., & Xia, W. (2024). Tensorized Label Learning on Anchor Graph. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 13537-13544. https://doi.org/10.1609/aaai.v38i12.29257
Issue
Section
AAAI Technical Track on Machine Learning III