Multi-view learning aims to integrate multiple data information from different views to improve the learning performance. The key problem is to handle the unconformities or distortions among view-specific samples or measurements of similarity or dissimilarity. This paper models the view-specific samples as a nonlinear mapping of uniform but latent intact samples for all the views, and the view-specific dissimilarity matrices or similarity matrices are estimated in terms of the uniform latent one. Two methods are then developed for multi-view clustering. One makes use of uniform multidimensional scaling (UMDS) on multi-view dissimilarities or kernels. The other one uses a uniform class assignment (UCA) procedure that optimally extracts the cluster components contained in the view-specific similarity matrices. These two methods result in the same optimization model, subjected to some slightly different constraints. A first-order condition of solutions is given as a nonlinear eigenvalue problem, and a second order condition guarantees local optimality. The nonlinear eigenvalue problem is solved by an iterative algorithm via eigen-space updating, and its convergence is proven. Furthermore, a fast implementation of the algorithm is discussed, which adopts the strategy of restarting subspace extension. Numerical experiments on some real-world data sets provide good support to the proposed methods.