New Derivation for Gaussian Mixture Model Parameter Estimation: MM Based Approach
In this letter, we revisit the problem of maximum likelihood estimation (MLE) of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its parameters. The new derivation, unlike the classical approach employing the technique of expectation-maximization (EM), is straightforward and...
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| creator | Sahu, Nitesh Babu, Prabhu |
| description | In this letter, we revisit the problem of maximum likelihood estimation (MLE)
of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its
parameters. The new derivation, unlike the classical approach employing the
technique of expectation-maximization (EM), is straightforward and doesn't
invoke any hidden or latent variables and calculation of the conditional
density function. The new derivation is based on the approach of
minorization-maximization and involves finding a tighter lower bound of the
log-likelihood criterion. The update steps of the parameters, obtained via the
new derivation, are same as the update steps obtained via the classical EM
algorithm. |
| doi_str_mv | 10.48550/arxiv.2001.02923 |
| format | Article |
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of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its
parameters. The new derivation, unlike the classical approach employing the
technique of expectation-maximization (EM), is straightforward and doesn't
invoke any hidden or latent variables and calculation of the conditional
density function. The new derivation is based on the approach of
minorization-maximization and involves finding a tighter lower bound of the
log-likelihood criterion. The update steps of the parameters, obtained via the
new derivation, are same as the update steps obtained via the classical EM
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of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its
parameters. The new derivation, unlike the classical approach employing the
technique of expectation-maximization (EM), is straightforward and doesn't
invoke any hidden or latent variables and calculation of the conditional
density function. The new derivation is based on the approach of
minorization-maximization and involves finding a tighter lower bound of the
log-likelihood criterion. The update steps of the parameters, obtained via the
new derivation, are same as the update steps obtained via the classical EM
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of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its
parameters. The new derivation, unlike the classical approach employing the
technique of expectation-maximization (EM), is straightforward and doesn't
invoke any hidden or latent variables and calculation of the conditional
density function. The new derivation is based on the approach of
minorization-maximization and involves finding a tighter lower bound of the
log-likelihood criterion. The update steps of the parameters, obtained via the
new derivation, are same as the update steps obtained via the classical EM
algorithm.</abstract><doi>10.48550/arxiv.2001.02923</doi><oa>free_for_read</oa></addata></record> |
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| title | New Derivation for Gaussian Mixture Model Parameter Estimation: MM Based Approach |
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