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Most of the researchers in the application areas usually use the EM algorithm to find estimators of the normal mixture distribution with unknown component specific variances without knowing much about the properties of the estimators.
Such a large and geographically widespread integrated coastal management project is bound to have site specific variances due to the presence or absence of certain community characteristics as well as differences between project personnel.
Several methods have been suggested for modification of gene specific variances or covariances to improve the estimation.
For shrinkage estimation of both means and variances, our objective first is to obtain smooth estimation of gene specific variances and then to use estimated variances in a hierarchical model assuming it is known.
In other words, the covariance structure of D did not include specific variances.
These models are denoted as FARR k models, for a k-dimensional FA model with all specific variances constrained to be zero.
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They assume that uncertainty contributions – having heterogeneous, datum-specific, variances – might not be included in the error budgets of some of the measured values.
Thus, in this paper, we propose scaled norm-based Euclidean projection (SNEP) which is a generalized version of EPL1, utilizing dimension-specific variances.
Hence, every component in EPL1 is equally treated for optimization without considering any scaling parameters such as dimension-specific variances and standard deviations.
In Section 4, we describe the procedure for EPL1-based speaker adaptation which is unable to consider the dimension-specific variances.
To make EPL1 more flexible, in this paper, we propose scaled norm-based Euclidean projection (SNEP) which can consider dimension-specific variances.
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