A Fuzzy Least Square Support Tensor Machines Based on Support Vector Data Description

Most of the traditional machine learning algorithms are based on the vector, but in tensor space, Tensor learning is useful to overcome the over fitting problem in vector-based learning. In the meanwhile, tensor-based algorithm requires a smaller set of decision variables as compared to vector-based approaches. We also would require that the meaningful tensor training points must be classified correctly and would not care about some training points like noises whether or not they are classified correctly. To utilize the structural information present in high dimensional features of an object and fuzzy membership, this paper presents a novel fuzzy classifier for Image processing based on support vector data description (SVDD), termed as Fuzzy Least Squares support tensor machine (FLSSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of FLSSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in ORL database and Yale database.