Multi-View Unsupervised Feature Selection with Dynamic Sample Space Structure

With the rapid growth of complex high-dimensional sparse data and the limitation of a single perspective, there is an increasing demand for new methods of feature selection from multiple perspectives. The feature selection method based on minimum regression is usually learning projection matrix, which is lack of theoretical explanation to evaluate the importance of features. Moreover, these methods cannot find the global and sparse solutions of the projection matrix. In this paper, we propose a new multi-view unsupervised feature selection method, which can learn the global and sparse solutions of the projection matrix. The new method extends the least squares regression model by adjusting the regression coefficients in least squares regression by using a set of scale factors for feature ranking. It shows that the new model can learn global and sparse solutions. In addition, the introduction of the scale factor provides a theoretical explanation for why we can use the projection matrix to sort features. In order to optimize the new model, a simple and effective algorithm for proof convergence is proposed. By classifying and clustering the data after feature selection, we can see the superiority of our algorithm.