In order to solve the problem of unstable sparseness of Non-negative Matrix Factorization (NMF), an improved NMF on orthogonal subspace with L1 norm constraints was proposed. L1 norm constrained was introduced into the objective function of NMF on Orthogonal Subspace (NMFOS), which enhanced the sparsity of the decomposition results. The multiplicative updating procedure was also produced. Experiments on UCI, ORL, and Yale show that this algorithm is superior to other algorithms in clustering and sparse representation.