Robust Unsupervised Clustering Using Generalized Annealing M-estimator
نویسندگان
چکیده
A new robust clustering algorithm, called generalized annealing M-estimator (GAM-estimator), is proposed. Initialized with multiple seeds, the GAM-estimator converges to several optimal cluster centers. Neither knowledge about the number of clusters nor scale is needed. The global optimal solution of clustering is achieved by minimization of an objective function. The algorithm is applied to unsupervised texture segmentation and texture-based defect detection .
منابع مشابه
A robust wavelet based profile monitoring and change point detection using S-estimator and clustering
Some quality characteristics are well defined when treated as response variables and are related to some independent variables. This relationship is called a profile. Parametric models, such as linear models, may be used to model profiles. However, in practical applications due to the complexity of many processes it is not usually possible to model a process using parametric models.In these cas...
متن کاملRobustizing robust M-estimation using deterministic annealing
Robustizing Robust M-Estimation Using Deterministic Annealing S. Z. Li School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 [email protected] ABSTRACT This paper presents a modi ed robust M-estimator referred to as annealing M-estimator (AM-estimator) to avoid problems with M-estimator. The AM-estimator combines the annealing technique into the...
متن کاملA Two-Phase Robust Estimation of Process Dispersion Using M-estimator
Parameter estimation is the first step in constructing any control chart. Most estimators of mean and dispersion are sensitive to the presence of outliers. The data may be contaminated by outliers either locally or globally. The exciting robust estimators deal only with global contamination. In this paper a robust estimator for dispersion is proposed to reduce the effect of local contamination ...
متن کاملModel Selection in Clustering by Uniform Convergence Bounds
Unsupervised learning algorithms are designed to extract structure from data samples. Reliable and robust inference requires a guarantee that extracted structures are typical for the data source, Le., similar structures have to be inferred from a second sample set of the same data source. The overfitting phenomenon in maximum entropy based annealing algorithms is exemplarily studied for a class...
متن کاملDiscrete time robust control of robot manipulators in the task space using adaptive fuzzy estimator
This paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. A novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. Parameters of the fuzzy estimator are adapted to minimize the estimat...
متن کامل