A New Method for Multi-Models Estimation
Be dead against the fact that most of algorithms are incapable to solve Multi-Models Estimation effectually,a new method is provided that estimate multi-models with Model-Based Clustering in Conceptual Space.Start from that each data point is represented with a Preference Set of hypotheses models preferred by that poin,and then,the Jaccard distance between two Preference Sets is described as a attribute of a data point, finally, to perform a clustering operation using the improved Cobweb algorithm based on the attribute of the data points.Neither this method requires prior specification of the number of models, nor it necessitate parameters transform,so that it can overcome missing detection and false detection of crossing models.
Y. Y. Yu and Z. J. Wang, "A New Method for Multi-Models Estimation", Applied Mechanics and Materials, Vols. 138-139, pp. 1288-1293, 2012