Abstract: The unsupervised feature selection method based on spectrogram is constructed in the original high dimensional data space, which is easily disturbed by noise or redundant features. To overcome these deficiencies, an unsupervised feature selection method based on adaptive locality preserving projection is proposed. Global linear regression function is utilized to construct feature selection model, and the adaptive local preserving projection is adopted to improve model accuracy. Then the l2,1-norm constraint is added to improve the distinguishability of different features and avoid noise interference. A comparison with several state-of-the-art feature selection methods demostrate the effectiveness of the proposed method.