Feature selection is a machine learning technique that selects a representative subset of all features available in order to reduce the time and space needed to process high-dimensional data. Traditional feature selection methods include filter, wrapper, and embedded approaches. However, many conventional methods' performances are not suitable in many contexts. This paper proposes a new unsupervised feature selection model based on pseudo label approximation. The new derived model incorporates a projection error, a sparsity regularization, and a manifold regularization term that preserves the manifold structure of the original data. Finally, implementation of the new model onto five distinct datasets validates the effectiveness of the proposed model. © 2021 ACM.