Timeline for When to use Gaussian mixture model?
Current License: CC BY-SA 3.0
17 events
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May 4, 2018 at 18:17 | review | Suggested edits | |||
May 4, 2018 at 19:04 | |||||
Dec 17, 2017 at 20:23 | answer | added | mynameisvinn | timeline score: 0 | |
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Feb 6, 2017 at 5:20 | comment | added | Vinay | @arpit-sisodia, Link you have provided suggests more of trial and error method to see if GMM fits to my data. Is there a conclusive way/Thumb rule to decide up on the models to use. Trial-and-error method of playing with more mixtures can fit my data. But is there a certain way of deciding up on? Like we need to have Linear separability of data for SVM classification | |
Feb 6, 2017 at 5:20 | comment | added | Vinay | @arpit-sisodia, We are working on feasibility of a hardware keyboard setup which seem to have specific features and we are planning to model it using GMM. We dont know the underlying process clearly and hence we are trying to model using machine learning methods. So, we are not sure if there is actually a mixture of gaussians in the underlying process. Moreover, it is multi-dimensional and we cannot visualize it to see if it is mixture of gaussians | |
Feb 6, 2017 at 1:52 | history | tweeted | twitter.com/StackStats/status/828421080436072450 | ||
Feb 5, 2017 at 18:12 | comment | added | gung - Reinstate Monica | You can think of it as a form of clustering where you don't have labeled data & believe the latent groupings are perfectly multivariate normal. | |
Feb 5, 2017 at 18:08 | comment | added | Arpit Sisodia | what is ur data set and what is your exact problem? It's used when data follows ( is a mixture of) more than 1 normal distribution. See another question -stats.stackexchange.com/questions/236295/… | |
Feb 5, 2017 at 18:00 | answer | added | Slayer | timeline score: 4 | |
Feb 5, 2017 at 17:50 | history | edited | Franck Dernoncourt | CC BY-SA 3.0 |
edited tags; edited title
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Feb 5, 2017 at 17:49 | review | First posts | |||
Feb 5, 2017 at 18:12 | |||||
Feb 5, 2017 at 17:47 | history | asked | Vinay | CC BY-SA 3.0 |