Unsupervised Hierarchical Feature Selection on Networked Data

Abstract

Networked data is commonly observed in many high-impact domains, ranging from social networks, collaboration platforms to biological systems. In such systems, the nodes are often associated with high dimensional features while remain connected to each other through pairwise interactions. Recently, various unsupervised feature selection methods have been developed to distill actionable insights from such data by finding a subset of relevant features that are highly correlated with the observed node connections. Although practically useful, those methods predominantly assume that the nodes on the network are organized in a flat structure, which is rarely the case in reality. In fact, the nodes in most, if not all, of the networks can be organized into a hierarchical structure. For example, in a collaboration network, researchers can be clustered into different research areas at the coarsest level and are further specified into different sub-areas at a finer level. Recent studies have shown that such hierarchical structure can help advance various learning problems including clustering and matrix completion. Motivated by the success, in this paper, we propose a novel unsupervised feature selection framework (HNFS) on networked data. HNFS can simultaneously learn the implicit hierarchical structure among the nodes and embed the hierarchical structure into the feature selection process. Empirical evaluations on various real-world datasets validate the superiority of our proposed framework.