Manifold Alignment by Scalable Constraints of the Point Clouds

The correspondence leaning of two high-dimensional data sets via the manifold leaning techniques is even more important in recent years. It’s convenient for us to find the shared latent structure of the high-dimensional data sets, if they can be aligned in a uniformed low-dimensional data space. In this paper, we propose an algorithm to solve this problem via Scalable Constraints of the Point Clouds(SCPC). SCPC is used here as a method to find the inner manifold constraint of each dataset. A cost function to measure the quality of alignment is given by combining the inner manifold constraints of each dataset and the matching points constraints among different datasets. The effectiveness of our algorithm is validated by applying it to the problem of image sequences alignment.