In this contribution, we introduce a multilevel approximation method with T-splines for fitting scattered point clouds iteratively, an application to land remote sensing. This new procedure provides local surface by explicit computation of the control points and is called (MTA). It computationally efficient compared traditional global least-squares (LS) approach, which may fail when there unfav...

The ill conditioning problem of sensor registration is considered. We analyze the ill conditioning in the dense-target scenario and the dense-sensor scenario, respectively, and present a robust registration method based on the bounded variables least squares (BVLS). The proposed approach can reduce the influence of ill conditioning by means of inserting prior constraints on the desired solution...

For a small sample problem with a large number of features, feature selection by cross-validation frequently goes into random tie breaking because of the discrete recognition rate. This leads to inferior feature selection results. To solve this problem, we propose using a least squares support vector regressor (LS SVR), instead of an LS support vector machine (LS SVM). We consider the labels (1...

Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem. However, sparseness is lost in the LS-SVM case and the estimation of the support values is only optimal in the...