@article{Huang_Xia_Sun_Yamada_Chang_2020, title={Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5839}, DOI={10.1609/aaai.v34i04.5839}, abstractNote={<p>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 <em>linear</em> 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 <em>nonlinear</em> 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 <em>linear</em> unsupervised feature selection methods.</p>}, number={04}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Huang, Qiang and Xia, Tingyu and Sun, Huiyan and Yamada, Makoto and Chang, Yi}, year={2020}, month={Apr.}, pages={4182-4189} }