GMM Learning Method Based on CSMDEM Algorithm

To solve the over-splitting problem suffered in Mahalanobis distance based EM(MDEM) algorithm,a Competitive Stop MDEM(CSMDEM) algorithm is proposed.By regarding Minimum Description Length(MDL) criteria as a competitive stop condition and embedding it into MDEM algorithm,the CSMDEM algorithm can select model order while estimating the parameters of GMM.Experimental results show that the proposed CSEM algorithm has an increased capability to fit GMM while maintaining a low average number of EM iterations.By applying it to signal sorting,the proposed EM algorithm can sort FH signals with high correctness.

[1]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  Constantine Kotropoulos,et al.  Gaussian Mixture Modeling by Exploiting the Mahalanobis Distance , 2008, IEEE Transactions on Signal Processing.

[3]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[7]  Chi Fang,et al.  Histogram of the oriented gradient for face recognition , 2011 .

[8]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[9]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).