Robust Graph Embedding with Noisy Link Weights

Akifumi Okuno, Hidetoshi Shimodaira
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:664-673, 2019.

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 this Paper

BibTeX
@InProceedings{pmlr-v89-okuno19b, title = {Robust Graph Embedding with Noisy Link Weights}, author = {Okuno, Akifumi and Shimodaira, Hidetoshi}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {664--673}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/okuno19b/okuno19b.pdf}, url = {https://proceedings.mlr.press/v89/okuno19b.html}, 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.} }
Endnote
%0 Conference Paper %T Robust Graph Embedding with Noisy Link Weights %A Akifumi Okuno %A Hidetoshi Shimodaira %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-okuno19b %I PMLR %P 664--673 %U https://proceedings.mlr.press/v89/okuno19b.html %V 89 %X 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.
APA
Okuno, A. & Shimodaira, H.. (2019). Robust Graph Embedding with Noisy Link Weights. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:664-673 Available from https://proceedings.mlr.press/v89/okuno19b.html.

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