Online algorithm based on support vectors for orthogonal regression

In this paper, we introduce a new online algorithm for orthogonal regression. The method is constructed via an stochastic gradient descent approach combined with the idea of a tube loss function, which is similar to the one used in support vector (SV) regression. The algorithm can be used in primal or in dual variables. The latter formulation allows the introduction of kernels and soft margins. In addition, an incremental strategy algorithm is introduced, which can be used to find sparse solutions and also an approximation to the minimal tube containing the data. The algorithm is very simple to implement and avoids quadratic optimization. (c) 2013 Published by Elsevier B.V.