Sparsity-Aware Possibilistic Clustering Algorithms
نویسندگان
چکیده
منابع مشابه
Convergence Theorems of Possibilistic Clustering Algorithms and Generalized Possibilistic Clustering Algorithms
A generalized approach to possibilistic clustering algorithms was proposed in [19], where the memberships are evaluated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. The computational experiments based on the generalized possibilistic clustering algorithms in [19] revealed that these clustering algorithms coul...
متن کاملA Generalized Approach to Possibilistic Clustering Algorithms
Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approache...
متن کاملSparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage
This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based on an alternating optimization strategy with shrinkage. The proposed scheme employs a two-stage structure that consists of an alternating optimization of a diagonally-structured matrix that speeds up the convergence and an adaptive filter with a shrinkage function that forces the coefficients with small m...
متن کاملSparsity-aware Adaptive Filtering Algorithms and Application to System Identification
In this thesis, low-complexity adaptive filtering algorithms that exploit the sparsity of signals and systems are derived and investigated. Specifically, sparsity-aware normalized least-mean square and affine projection algorithms are developed based on the l1-norm incorporated to their cost function, which we term zero-attracting NLMS (ZA-NLMS) and zero-attracting APA (ZA-APA). These algorithm...
متن کاملOn the convergence of some possibilistic clustering algorithms
In this paper, an analysis of the convergence performance is conducted for a class of possibilistic clustering algorithms utilizing the Zangwill convergence theorem. It is shown that under certain conditions the iterative sequence generated by a possibilistic clustering algorithm converges, at least along a subsequence, to either a local minimizer or a saddle point of the objective function of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Fuzzy Systems
سال: 2016
ISSN: 1063-6706,1941-0034
DOI: 10.1109/tfuzz.2016.2543752