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In this paper, we extend Gaussian graphical models to proper quaternion Gaussian distributions. The properness assumption reduces the number of unknowns by a factor of four while graphical models reduce the number of degrees of freedom via sparsity. Each of the methods allows accurate estimation using a small number of samples. To enjoy both gains, we show that the proper quaternion Gaussian inverse...
In this paper we extend Gaussian graphical models to proper quaternion Gaussian distributions. The properness assumption reduces the number of unknowns by a factor of four and allow for improved accuracy. We begin by showing that the unconstrained proper quaternion maximum likelihood problem is convex and has a closed form solution that resembles the classical sample covariance. Then, we proceed and...
We present a method that uses a set of maximum-likelihood (ML) trained discrete HMM models as a baseline system, and an SVM training scheme to re-score the results of the baseline HMMs. It turns out that the re-scoring model can be represented as an un-normalized HMM. We refer to these models as pseudo-HMMs. The pseudo-HMMs are in fact a generalization of standard HMMs, and by proper discriminative...
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