Fuzzy weighted support vector regression for multiple linear model estimation : application to object tracking in image sequences
Franck Dufrenois, Denis Hamad · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
In this paper, we present a new support vector regression (SVR) based strategy for simultaneously extracting multiple linear structures in a training data set. As in fuzzy c-prototypes algorithms [17], [18], [10], we introduce fuzzy weights in the SVR formulation which assign to each data point a membership value according to c-structures. We propose to solve the corresponding dual problem under an iterative strategy with an initialization step. Experiments show the benefits of robustness properties of SVR in comparison with the standard fuzzy c-prototypes algorithm. Next, the motion estimation problem is used to illustrate its applicability and relevance in respect of real-world applications.