Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Automatic Genre Classification of TV Programmes Using Gaussian Mixture Models and Neural Networks
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Type-2 fuzzy Gaussian mixture models
Pattern Recognition
Adaptive Gaussian Mixture Models Based Facial Actions Tracking
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 02
Recognition of Emotions in German Speech Using Gaussian Mixture Models
Multimodal Signals: Cognitive and Algorithmic Issues
MIMO-AR system identification and blind source separation for GMM-distributed sources
IEEE Transactions on Signal Processing
Recognizing visual focus of attention from head pose in natural meetings
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Fuzzy qualitative human motion analysis
IEEE Transactions on Fuzzy Systems
Driving profile modeling and recognition based on soft computing approach
IEEE Transactions on Neural Networks
Active curve axis Gaussian mixture models
Pattern Recognition
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A method for training finite mixture models under a fuzzy clustering principle
Fuzzy Sets and Systems
Gaussian Mixture Modeling by Exploiting the Mahalanobis Distance
IEEE Transactions on Signal Processing - Part I
Discriminatively Trained GMMs for Language Classification Using Boosting Methods
IEEE Transactions on Audio, Speech, and Language Processing
Wrapped Gaussian Mixture Models for Modeling and High-Rate Quantization of Phase Data of Speech
IEEE Transactions on Audio, Speech, and Language Processing
Efficient Speaker Change Detection Using Adapted Gaussian Mixture Models
IEEE Transactions on Audio, Speech, and Language Processing
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Regularized Linear Fuzzy Clustering and Probabilistic PCA Mixture Models
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Packet Video Error Concealment With Gaussian Mixture Models
IEEE Transactions on Image Processing
A Unified Fuzzy Framework for Human-Hand Motion Recognition
IEEE Transactions on Fuzzy Systems
Variational and PCA based natural image segmentation
Pattern Recognition
Prediction from expert demonstrations for safe tele-surgery
International Journal of Automation and Computing
Pair-copula based mixture models and their application in clustering
Pattern Recognition
| Hi-index | 0.01 |
In this paper, in order to improve both the performance and the efficiency of the conventional Gaussian Mixture Models (GMMs), generalized GMMs are firstly introduced by integrating the conventional GMMs and the active curve axis GMMs for fitting non-linear datasets, and then two types of Fuzzy Gaussian Mixture Models (FGMMs) with a faster convergence process are proposed based on the generalized GMMs, inspired from the mechanism of Fuzzy C-means (FCMs) which introduces the degree of fuzziness on the dissimilarity function based on distances. One is named as probability based FGMMs defining the dissimilarity as the multiplicative inverse of probability density function, and the other is distance based FGMMs which define the dissimilarity function focusing the degree of fuzziness only on the distances between points and component centres. Different from FCMs, both of the proposed dissimilarity functions are based on the exponential function of the distance. The FGMMs are compared with the conventional GMMs and the generalized GMMs in terms of the fitting degree and convergence speed. The experimental results show that the proposed FGMMs not only possess the non-linearity to fit datasets with curve manifolds but also have a much faster convergence process saving more than half computational cost than GMMs'.