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Unsupervised Learning: Difference between revisions
→Optimization
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<math> L(0) \geq L(1) \geq L(2) \geq ...</math><br> | <math> L(0) \geq L(1) \geq L(2) \geq ...</math><br> | ||
* Since the loss is monotone decreasing and bounded below, it will converge by the monotone convergence theorem. | * Since the loss is monotone decreasing and bounded below, it will converge by the monotone convergence theorem. | ||
* However, this does not imply that the parameters <math>\mathbf{\mu}</math> and <math>\mathbf{z}</ | * However, this does not imply that the parameters <math>\mathbf{\mu}</math> and <math>\mathbf{z}</math> will converge. | ||
====Algorithm==== | ====Algorithm==== | ||