  Unsupervised feature selection has drawn wide attention in the era of big
data since it is a primary technique for dimensionality reduction. However,
many existing unsupervised feature selection models and solution methods were
presented for the purpose of application, and lack of theoretical support,
e.g., without convergence analysis. In this paper, we first establish a novel
unsupervised feature selection model based on regularized minimization with
nonnegative orthogonal constraints, which has advantages of embedding feature
selection into the nonnegative spectral clustering and preventing overfitting.
An effective inexact augmented Lagrangian multiplier method is proposed to
solve our model, which adopts the proximal alternating minimization method to
solve subproblem at each iteration. We show that the sequence generated by our
method globally converges to a Karush-Kuhn-Tucker point of our model. Extensive
numerical experiments on popular datasets demonstrate the stability and
robustness of our method. Moreover, comparison results of algorithm performance
show that our method outperforms some existing state-of-the-art methods.
