Title :
Interval calculation of EM algorithm for GMM parameter estimation
Author :
Watanabe, Hidenori ; Muramatsu, Shogo ; Kikuchi, Hisakazu
Author_Institution :
Grad. Sch. of Sci. & Technol., Niigata Univ., Niigata, Japan
fDate :
May 30 2010-June 2 2010
Abstract :
This work proposes a low complexity computation of EM algorithm for Gaussian mixture model(GMM) and accelerates the parameter estimation. In previous works, the authors revealed that the computational complexity of GMM-based classification can be reduced by using an interval calculation technique. This work applies the idea to EM algorithm for GMM parameter estimation. From experiments, it is confirmed that the computational speed of the proposal achieves more than twice that of the standard method with ´exp( )´ function. The relative errors are less than 0.6% and 0.053% when the number of bits for table addressing are 4 and 8, respectively.
Keywords :
Gaussian processes; computational complexity; expectation-maximisation algorithm; learning (artificial intelligence); parameter estimation; EM algorithm; GMM based classification; GMM parameter estimation; Gaussian mixture model; computational complexity; expectation maximization algorithm; interval calculation technique; Acceleration; Clustering algorithms; Computational complexity; Computational efficiency; Covariance matrix; Gaussian distribution; Machine learning algorithms; Parallel processing; Parameter estimation; Proposals;
Conference_Titel :
Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-5308-5
Electronic_ISBN :
978-1-4244-5309-2
DOI :
10.1109/ISCAS.2010.5537044
