Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification
Abstract
Incomplete view information often results in failure cases of the conventional multi-view methods. To address this problem, we propose a Deep Correlated Predictive Subspace Learning (DCPSL) method for incomplete multi-view semi-supervised classification. Specifically, we integrate semi-supervised deep matrix factorization, correlated subspace learning, and multi-view label prediction into a unified framework to jointly learn the deep correlated predictive subspace and multi-view shared and private label predictors. DCPSL is able to learn proper subspace representation that is suitable for class label prediction, which can further improve the performance of classification. Extensive experimental results on various practical datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.
Cite
Text
Xue et al. "Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/559Markdown
[Xue et al. "Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/xue2019ijcai-deep/) doi:10.24963/IJCAI.2019/559BibTeX
@inproceedings{xue2019ijcai-deep,
title = {{Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification}},
author = {Xue, Zhe and Du, Junping and Du, Dawei and Ren, Wenqi and Lyu, Siwei},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {4026-4032},
doi = {10.24963/IJCAI.2019/559},
url = {https://mlanthology.org/ijcai/2019/xue2019ijcai-deep/}
}