APPLICATION OF SUPERVISED LEARNING
UNSUPERVISED LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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What does GMM-EM optimise?
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Minimises the average distance between the samples and the mean of the nearest Gaussian
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Minimises the negative-log-likelihood of the model
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Maximises the classification rate
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None of the above
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Explanation:
Detailed explanation-1: -Gaussian Mixture models are used for representing Normally Distributed subpopulations within an overall population. The advantage of Mixture models is that they do not require which subpopulation a data point belongs to. It allows the model to learn the subpopulations automatically.
Detailed explanation-2: -GMM has many applications, such as density estimation, clustering, and image segmentation. For density estimation, GMM can be used to estimate the probability density function of a set of data points. For clustering, GMM can be used to group together data points that come from the same Gaussian distribution.
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