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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks

February 1, 2023

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Authors

Qiang Huang

Jilin University


Tingyu Xia

Jilin University


Huiyan Sun

Jilin University


Makoto Yamada

Kyoto University


Yi Chang

Jilin University


DOI:

10.1609/aaai.v34i04.5839


Abstract:

With the rapid development of social media services in recent years, relational data are explosively growing. The signed network, which consists of a mixture of positive and negative links, is an effective way to represent the friendly and hostile relations among nodes, which can represent users or items. Because the features associated with a node of a signed network are usually incomplete, noisy, unlabeled, and high-dimensional, feature selection is an important procedure to eliminate irrelevant features. However, existing network-based feature selection methods are linear methods, which means they can only select features that having the linear dependency on the output values. Moreover, in many social data, most nodes are unlabeled; therefore, selecting features in an unsupervised manner is generally preferred. To this end, in this paper, we propose a nonlinear unsupervised feature selection method for signed networks, called SignedLasso. This method can select a small number of important features with nonlinear associations between inputs and output from a high-dimensional data. More specifically, we formulate unsupervised feature selection as a nonlinear feature selection problem with the Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso), which can find a small number of features in a nonlinear manner. Then, we propose the use of a deep learning-based node embedding to represent node similarity without label information and incorporate the node embedding into the HSIC Lasso. Through experiments on two real world datasets, we show that the proposed algorithm is superior to existing linear unsupervised feature selection methods.

Topics: AAAI

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HOW TO CITE:

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks Proceedings of the AAAI Conference on Artificial Intelligence (2020) 4182-4189.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks AAAI 2020, 4182-4189.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang (2020). Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 4182-4189.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang. Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.4182-4189.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang. 2020. Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 4182-4189.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang. (2020) "Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.4182-4189

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang, "Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks", AAAI, p.4182-4189, 2020.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang. "Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.4182-4189.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang. "Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 4182-4189.

Qiang Huang||Tingyu Xia||Huiyan Sun||Makoto Yamada||Yi Chang. Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks. AAAI[Internet]. 2020[cited 2023]; 4182-4189.


ISSN: 2374-3468


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