Abstract: Feature selection is designed to select the relevant feature subset f rom the original features，which can facilitate data clustering，classification and retrieval.The most existing unsupervised feature selection algorithms establish a mathematical model by casting high－dimensional data into low－dimensional space.The scores for each feature are computed independently to select the top－ranked features.In this paper，we propose an unsupervised feature selection via sparse representation clustering.Firstly，the data of high－dimensional space is mapped into low－dimensional space by the Laplacian eigenmaps of manifold learning.Specifically，we construct a nearest neighbor graph with the number of samples.The low－dimensional space is found by embedding the graph，and the manifold structure of the original dataset is maintained.Secondly，we obtain the “flat” embedded matrix，which measures the importance of each feature and differentiates the contribution of each feature for each cluster.We can construct an objective function based on the low－dimensional space to fit high－dimensional space.The Least Angel Regression algorithm can be used to solve the optimization regression problem.We perform L1－norm sparse regression to accurately estimate the importance of features instead of evaluating the contribution of each feature，respectively.We can achieve the top－ranked features according to their finals－cores.Experimental results on six real－life datasets show that the proposed algorithm achieves good experimental results in clustering and mutual information.It can effectively select the important features and has good performance in the dimension reduction.In addition，the proposed algorithm is superior to several typical feature selection algorithms in the experimental process.