Multiview representations reveal the latent information of the data from different perspectives, consistency, and complementarity. Unlike most multiview learning approaches, which focus only one perspective, in this paper, we propose a novel unsupervised multiview learning algorithm, called comprehensive latent factor learning (CLFL), which jointly exploits both consistent and complementary information among multiple views. CLFL adopts a non-negative matrix factorization based formulation to learn the latent factors. It learns the weights of different views automatically which makes the representation more accurate. Experiment results on a synthetic and several real datasets demonstrate the effectiveness of our approach.