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lecture20

# lecture20 - CSE 6740 Lecture 20 How Do I Optimize...

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Unformatted text preview: CSE 6740 Lecture 20 How Do I Optimize Convex/Linear Functions? (Unconstrained Linear Optimization) Alexander Gray [email protected] Georgia Institute of Technology CSE 6740 Lecture 20 – p. 1/4 5 Today 1. Unconstrained Optimization: Latent-Variable 2. Convex Optimization Problems 3. Unconstrained Optimization: Linear Algebraic CSE 6740 Lecture 20 – p. 2/4 5 Unconstrained Optimization: Latent-Variable The EM algorithm, a form of bound optimization. CSE 6740 Lecture 20 – p. 3/4 5 Mixture of Gaussians Recall the mixture of Gaussians model, whose “hidden” variable is the class label: P ( C = k ) = π k , summationdisplay k π k = 1 (1) f ( X | C = k ) = N ( μ k , Σ 2 k ) (2) CSE 6740 Lecture 20 – p. 4/4 5 Mixture of Gaussians Recall the mixture of Gaussians model, whose “hidden” variable is the class label: P ( C = k ) = π k , summationdisplay k π k = 1 (3) f ( X | C = k ) = N ( μ k , Σ 2 k ) (4) f ( X ) = K summationdisplay k =1 f ( X | C = k ) P ( C = k ) = K summationdisplay k =1 π k N ( μ k , Σ 2 k ) (5) CSE 6740 Lecture 20 – p. 5/4 5 Mixture of Gaussians Recall Bayes rule, which gives P ( C = k | x ) = f ( x | C = k ) P ( C = k ) f ( x ) . (6) This value is the probability that a particular component k was responsible for generating the point x , and satisfies ∑ K k =1 P ( C = k | x ) = 1 . We’ll use as a shorthand w ik ≡ P ( C = k | x i ) . (7) CSE 6740 Lecture 20 – p. 6/4 5 Mixture of Gaussians We’ll consider a simplified case where the covariances are fixed to be diagonal with all dimensions equal, Σ k = σ 2 k I , so f ( x | C = k ) = N ( μ k , Σ k ) = 1 (2 πσ 2 k ) D/ 2 exp braceleftbigg − || x − μ k || 2 2 σ 2 k bracerightbigg (8) and f ( x ) = K summationdisplay k =1 π k 1 (2 πσ 2 k ) D/ 2 exp braceleftbigg − || x − μ k || 2 2 σ 2 k bracerightbigg . (9) CSE 6740 Lecture 20 – p. 7/4 5 inimizing the Negative Log-likelihood It is equivalent to minimize the negative log-likelihood E ≡ − log L ( θ ) = − N summationdisplay i =1 log f θ ( X i ) (10) = − N summationdisplay i =1 log parenleftBigg K summationdisplay k =1 f ( X i | C = k ) P ( C = k ) parenrightBigg . (11) Since this error function is a smooth differentiable function of the parameters, we can employ its derivatives to perform unconstrained optimization on it. CSE 6740 Lecture 20 – p. 8/4 5 EM: Recurrence Idea Now let’s revisit the EM algorithm, to how to derive it from a more fundamental principle, that of bound optimization . We can write the change in error on each iteration in the form E new − E old = − summationdisplay i log parenleftbigg f new ( x i ) f old ( x i ) parenrightbigg (12) = − summationdisplay i log parenleftbigg∑ k f new ( x i | C = k ) P new ( C = k ) f old ( x i ) P old ( C = k | x i ) P old ( C = k | x i ) parenrightbigg (13) where the last factor is simply the identity....
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lecture20 - CSE 6740 Lecture 20 How Do I Optimize...

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