cs401r_w2016:lab13
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cs401r_w2016:lab13 [2016/02/08 23:16] admin |
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| [[http://hatch.cs.byu.edu/courses/stat_ml/old_faithful.mat|Old Faithful dataset]] | [[http://hatch.cs.byu.edu/courses/stat_ml/old_faithful.mat|Old Faithful dataset]] | ||
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| + | **The first thing you should is mean-center your data (ie, compute the mean of the data, then subtract that off from each datapoint).** (If you don't do this, you'll get zero probabilities for all of your responsibilities, given the initial conditions discussed below.) | ||
| The equations for implementing the EM algorithm are given in MLAPP 11.4.2.2 - 11.4.2.3. | The equations for implementing the EM algorithm are given in MLAPP 11.4.2.2 - 11.4.2.3. | ||
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| - Compute the responsibilities $r_{ik}$ (Eq. 11.27) | - Compute the responsibilities $r_{ik}$ (Eq. 11.27) | ||
| - Update the mixing weights $\pi_k$ (Eq. 11.28) | - Update the mixing weights $\pi_k$ (Eq. 11.28) | ||
| - | - Update the means $\mu_k$ (Eq. 11.31) | ||
| - Update the covariances $\Sigma_k$ (Eq. 11.32) | - Update the covariances $\Sigma_k$ (Eq. 11.32) | ||
| + | - Update the means $\mu_k$ (Eq. 11.31) | ||
| - | Now, repeat until convergence. | + | Now, repeat until convergence. Note that if you change the order of operations, you may get slightly difference convergences than the reference image. |
| Since the EM algorithm is deterministic, and since precise initial conditions for your algorithm are given below, the progress of your algorithm should closely match the reference image shown above. | Since the EM algorithm is deterministic, and since precise initial conditions for your algorithm are given below, the progress of your algorithm should closely match the reference image shown above. | ||
cs401r_w2016/lab13.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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