Unsupervised Feature Selection via Metric Fusion and Novel Low-Rank Approximation

Unsupervised feature selection aims to derive a compact set of features with desired generalization ability via removing the irrelevant and redundant features, yet challenging due to the unavailability of labels. Works about unsupervised feature selection always need to construct the similarity matrix, which makes the selected features highly depend on the accuracy of similarity measurement. However, existing works usually leverage a single fixed metric to build similarity matrix, which cannot fit various feature types very well and even damage the local manifold structure. To address this problem, we propose an adaptive multi-metric fusion by automatically integrating similarity across different metrics according to the specific data. Besides, to capture the global structure more precisely, a novel low-rank approximation method is proposed, which is relatively insensitive to the rank-norm parameter. Via the proposed novel low-rank approximation method, better tradeoff between the performance and robustness can be provided. Experimental results show that the accuracy performance of the proposed method can be boosted by
$2\%-11\%$
, compared with previous methods.