# Latent Multi-view Semi-Supervised Classification

arXiv: https://arxiv.org/abs/1909.03712 · HTML: https://mlsanity.com/paper/1909.03712/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [S-multi-SNE: Semi-Supervised Classification and Visualisation of Multi-View Data](https://mlsanity.com/paper/2111.03519/) (2021)
- [Multi-view Data Classification with a Label-driven Auto-weighted Strategy](https://mlsanity.com/paper/2201.00714/) (2022)

## Abstract (arXiv)

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.