Nonlinear approximations for motion and subspace segmentation

The motion segmentation problem is a special case of the general subspace segmentation problem that clusters data drawn from an unknown union of subspaces. This paper provides a nonlinear model for general subspace segmentation problem and presents an algorithm to compute the optimal solution for noiseless data. We also provide a combined algorithm that addresses issues with noise to some extent. Furthermore, a devised algorithm that specifically targets motion segmentation has been developed and applied to the Hopkins 155 Dataset. It generates the best segmentation rate to the date.