Robust low rank approximation is central to many computer version and data mining domains. Although numerous algorithms have been developed to cope with this issue, most of them considered only the setting that the input data matrix is contaminated by sparse noise and ignored the existing of column-outliers. Here, the outliers represent the columns corrupted by noise completely. To recover the low rank component of a data matrix contaminated by sparse noise and outliers simultaneously, in this paper we first implement a novel robust low rank approximation model to recover a low rank matrix L, and then utilize the subspace obtained by conducting SVD on L to estimate the low rank component of residual data. Numer-ical experiments on real data and synthetic data demonstrate the superiority of proposed method.