Pattern Recognition and Machine Learning
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150 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...3) Setting of Hyper-Parameters: We set all the hyperparameters involved in our model in a noninformative manner to make them possibly less affect the inference of posterior distributions [3]....
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...We use variational Bayes (VB) method [3] for posterior inference....
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150 citations
150 citations
Cites background from "Pattern Recognition and Machine Lea..."
...Note that y∗ conditioned by y also follows the normal distribution [31]....
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150 citations
Cites background or methods from "Pattern Recognition and Machine Lea..."
...In the model-based techniques, standard Gaussian mixture model (GMM) [8]–[10], [18], [19], is a wellknown method that has been widely used due to its simplicity and ease of implementation....
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...In order to partition an image consisting of N pixels into K labels, GMM [10], [29] assumes that each observation xi is considered independent of the label j ....
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...Over the past decades, a number of algorithms based on the model-based techniques [8]–[10] have been proposed....
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150 citations
Cites background or methods or result from "Pattern Recognition and Machine Lea..."
...3 Comparison of the Reviewed Solution with the Present Work In order to compare the solution of [1] reviewed in Sect....
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...4 Review of a Recent Solution The most recent PCA solution in the framework of approximation error minimization, derived in [1], is reviewed here....
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...(29) If ̃ W ̃ W = B, we have the approximation according to [1] in (10) of Sect....
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...But [1] necessitates an orthogonal projection of certain data-independent components b ∈ Rp−q to μ ∈ R to achieve the same objective....
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...We will also be reviewing [1] who derives PCA in the same framework as that of ours....
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