Robust Graph Embedding with Noisy Link Weights
Abstract
We propose $\beta$-graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment $\beta$-score reduces the influence of contamination and robustly measures the difference between the underlying correct expected weights of links and the specified generative model. The proposed method is computationally tractable; we employ a minibatch-based efficient stochastic algorithm and prove that this algorithm locally minimizes the empirical moment $\beta$-score. We conduct numerical experiments on synthetic and real-world datasets.
Cite
Text
Okuno and Shimodaira. "Robust Graph Embedding with Noisy Link Weights." Artificial Intelligence and Statistics, 2019.Markdown
[Okuno and Shimodaira. "Robust Graph Embedding with Noisy Link Weights." Artificial Intelligence and Statistics, 2019.](https://mlanthology.org/aistats/2019/okuno2019aistats-robust/)BibTeX
@inproceedings{okuno2019aistats-robust,
title = {{Robust Graph Embedding with Noisy Link Weights}},
author = {Okuno, Akifumi and Shimodaira, Hidetoshi},
booktitle = {Artificial Intelligence and Statistics},
year = {2019},
pages = {664-673},
volume = {89},
url = {https://mlanthology.org/aistats/2019/okuno2019aistats-robust/}
}