As the ability to collect and store data improving, real data are usually made up of different forms (view). Therefore, multi-view learning plays a more and more important role in the field of machine learning and pattern recognition. In recent years, a variety of multi-view learning methods have been proposed and applied to different practical scenarios. However, since most of the data points in the objective function have square residuals and a few outliers with large errors can easily

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... ate the objective function, how to deal with redundant data becomes an important challenge for multi-view learning. For solving the above problems, this paper proposes a model, termed as robust auto-weighted multi-view subspace clustering. The model uses the Frobenius norm to deal with the squared error of data and uses the [?1]-norm to deal with outliers at the same time. Thus the effect of outliers and data points on model performance is effectively balanced. Furthermore, unlike traditional methods which measure the impact of different views by introducing hyper-parameters, the proposed model learns the weight of each view automatically. Since this model is a non-smooth and non-convex problem which is difficult to solve directly, this paper designs an effective algorithm to solve the problem and analyzes the convergence and computational complexity of this algo-rithm. Compared with traditional multi-view subspace clustering algorithms, the experimental results on multi-view datasets present the effectiveness of the proposed algorithm.