With the rapid development of information technology, massive amounts of multi-view data are constantly emerging in people's daily life. To cope with such situation, multi-view learning has received much attention in the field of machine learning to promote the ability of data understanding. However, due to the difficulties such as high cost and equipment failure in multi-view data collection, part or all of observed values from one view can't be available, which prevents some traditional multi-view learning algorithms from working effectively as expected. This paper focuses on the missing view completion for multi-view data and proposes a view compatibility based completion method. For each class of multi-view data, a corresponding shared subspace is built by means of supervised learning. With the multiple shared subspaces, a view compatibility discrimination model is developed. Meanwhile, assuming that the reconstruction error of each of view of multi-view data in the shared subspace takes the independent identical distribution, an approach is put forward to seek the shared representation of multi-view data with missing view. Thus, the preliminary completion of missing view can be performed. In addition, the multiple linear regression technique is implemented to obtain a more accurate completion. Furthermore, the proposed missing view completion method is enhanced to deal with the case of the denoising of noise-polluted multi-view data. The experimental results on some datasets including UCI and Coil-20 have demonstrated the effectiveness of the proposed missing view completion method for multi-view data.