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Efficient Low Dimensional Representation Of Vector Gaussian Distributions, Md Mahmudul Hasan

LSU Doctoral Dissertations

This dissertation seeks to find optimal graphical tree model for low dimensional representation of vector Gaussian distributions. For a special case we assumed that the population co-variance matrix $\Sigma_x$ has an additional latent graphical constraint, namely, a latent star topology. We have found the Constrained Minimum Determinant Factor Analysis (CMDFA) and Constrained Minimum Trace Factor Analysis (CMTFA) decompositions of this special $\Sigma_x$ in connection with the operational meanings of the respective solutions. Characterizing the CMDFA solution of special $\Sigma_x$, according to the second interpretation of Wyner's common information, is equivalent to solving the source coding problem of finding the minimum …