In this paper, we address the problem of semi-supervised feature selection from high-dimensional data. It aims to select the most discriminative and informative features for data analysis. This is a recent addressed challenge in feature selection research when dealing with small labeled data sampled with large unlabeled data in the same set. We present a filter based approach by constraining the known Laplacian score. We evaluate the relevance of a feature according to its locality preserving and constraints preserving ability. The problem is then presented in the spectral graph theory framework with a study of the complexity of the proposed algorithm. Finally, experimental results will be provided for validating our proposal in comparison with other known feature selection methods.