Unsupervised Feature Selection with Adaptive Structure Learning
This addresses a fundamental dilemma in unsupervised feature selection for data analysis, though it appears incremental as it builds on existing methods by integrating structure learning adaptively.
The paper tackles the chicken-and-egg problem in unsupervised feature selection, where accurate data structures are needed to select features, but those structures rely on having informative features already; the proposed method simultaneously learns structures and selects features, outperforming state-of-the-art methods on benchmark datasets.
The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods.