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Because of using the possibilistic partition to describe the data set, possibilistic clustering algorithm is more robust to noises than hard and fuzzy clustering algorithms. But calculating the membership matrix also makes it has a low efficiency. Moreover, the performance of possibilistic clustering may be degreased if the cluster number is set wrongly. In this paper, we proposed a new possibilistic...
Although fuzzy c-means classifier has been proved preferable to crisp ones and various types of fuzzy c-means classifiers have been designed, none of them are universal enough to perform equally well in all cases. A promising direction for more robust fuzzy c-means classification is to derive multiple candidate fuzzy c-means classification over a common dataset and then combine them into a consolidate...
Optimal discriminant plane based on Fisher criterion function is an important supervised feature extraction method and has great influence in the area of pattern recognition. In this paper, an extension of optimal discriminant plane in unsupervised pattern is presented. The basic idea is to optimize the defined fuzzy Fisher criterion function to figure out an optimal discriminant vector and fuzzy...
This paper presents a method to reconstruct the surface reflectance of objects from images. For objects with spatially-varying materials, we assume that each point can be represented as a weighted sum of several basic materials. First, a cluster algorithm is applied to automatically compute the number of basic materials. Then, the weight map and parameters of the reflectance model are iteratively...
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