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Unsupervised Learning: Difference between revisions
→Algorithm
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====Algorithm==== | ====Algorithm==== | ||
# Randomly initialize labels <math>\mathbf{z}</math>. | |||
# Then calculate the centroids <math>\mathbf{\mu}</math>. | |||
# Then update the labels for each example to the closest centroid. | |||
# Update the centroids by taking the mean of each point in the cluster. | |||
# Repeat steps 3 and 4 | |||
===Soft K-means=== | ===Soft K-means=== | ||
We will develop a model for how our data is generated:<br> | We will develop a model for how our data is generated:<br> | ||
Given <math>k</math> clusters, the probability of a point being from cluster k is <math>\phi_k = P(z^{(i)} = k)</math><br> | Given <math>k</math> clusters, the probability of a point being from cluster k is <math>\phi_k = P(z^{(i)} = k)</math><br> | ||