This paper presents an improved Unsupervised Simultaneous Orthogonal Basis Clustering Feature Selection (ISOCFS) method. In order to effectively select features for unlabeled data, a target matrix is used to design a regularized regression model. The target matrix obtains the potential clustering centers of the projected data points by orthogonal base clustering, and guides the projection matrix to select discriminative features. Unlike previous unsupervised feature selection methods, ISOCFS does not use the data points to pre-calculate local structure information to describe the objective function. Instead, it uses the target matrix to perform orthogonal basis clustering to directly calculate the potential clustering information. In addition, in order to reduce the interference of the noise to the estimation target matrix and the projection matrix, the method adds the noise term based on the previous method. Moreover, the method can be solved by using a simple optimization algorithm. Finally, four common microarray gene expression datasets and five recent unsupervised feature selection methods are utilized to demonstrate that the ISOCFS method can obtain better clustering results.