Estimation of ergodicity limits of bag-of-words modeling for guaranteed stochastic convergence. (March 2020) - Ghalyan, Ibrahim F. Jasim
Highlights: A dual ergodicity limits-based bag-of-words (DEL-BoW) technique is suggested that guarantees robustness against random initialization, estimates optimal model-order, and achieves enhanced modeling performance. In the DEL-BoW modeling technique, two limits are estimated to the random variable of the performance in order to guarantee ergodicity of the process. The first limit, with a larger radius of convergence, allows to have robustness against random initialization in the modeling process and helps in the estimation of optimal model-order. The second limit, with a smaller radius of convergence, helps in achieving enhanced modeling performance for the optimal model-order. The DEL-BoW modeling technique is applied to Caltech-101, Caltech-256, 15-Scenes, and Flower-102 datasets. Excellent modeling performance is reported when using DEL-BoW modeling technique to the aforementioned datasets. Abstract: This paper suggests an efficient dual ergodicity limits-based bag-of-words (DEL-BoW) modeling technique. The suggested DEL-BoW technique estimates two limits of ergodicity of a discrete random variable (drv) that is formed from the BoW classification performance of multiple runs. The first limit of ergodicity is estimated with a relatively larger ball of convergence to keep the drv shorter. Hence both robustness against random initialization and estimation of the optimal model-order are realized with a reduced number of iterations. Once the optimal model-order is estimated, the radius of ball of convergence is reduced and a second limit of ergodicity is estimated. Reducing the ball of convergence enlarges the size of the considered performance drv that enhances the classification performance. Experiments conducted on Caltech-101, Caltech-256, 15-Scenes, and Flower-102 datasets resulted in classification accuracy of 86.91%, 72.57%, 90.57%, and 90.86%, respectively. Comparison with state-of-the-art techniques shows the excellent performance of the DEL-BoW modeling process.