Title :
Real-Time Vector Quantization and Clustering Based on Ordinary Differential Equations
Author :
Cheng, Jie ; Sayeh, Mohammad R. ; Zargham, Mehdi R. ; Cheng, Qiang
Author_Institution :
Dept. of Comput. Sci., Univ. of Hawaii, Hilo, HI, USA
Abstract :
This brief presents a dynamical system approach to vector quantization or clustering based on ordinary differential equations with the potential for real-time implementation. Two examples of different pattern clusters demonstrate that the model can successfully quantize different types of input patterns. Furthermore, we analyze and study the stability of our dynamical system. By discovering the equilibrium points for certain input patterns and analyzing their stability, we have shown the quantizing behavior of the system with respect to its vigilance parameter. The proposed system is applied to two real-world problems, providing comparable results to the best reported findings. This validates the effectiveness of our proposed approach.
Keywords :
differential equations; nonlinear dynamical systems; pattern clustering; stability; vector quantisation; dynamical system; equilibrium points; ordinary differential equations; real-time clustering; stability; vector quantization; Eigenvalues and eigenfunctions; Mathematical model; Neural networks; Real time systems; Stability analysis; Vector quantization; Neural networks; ordinary differential equation-based clustering; real-time clustering; vector quantization; Algorithms; Artificial Intelligence; Computer Simulation; Computer Systems; Models, Theoretical; Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2011.2172627