1. cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14
7/29/2018
Today’s Topics
Learning Without a Teacher
K-Means Clustering
Hierarchical Clustering
Expectation-Maximization (using NB)
Auto-Association ANNs (formerly heavily used in Deep Neural Networks)
Read Section of Russell & Norvig plus "Standard Algorithm" section of the Wikipedia article on K-Means Clustering
12/8/16
cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

2. cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14
No teacher 
Unsupervised ML
Data abounds but labels hard to get, recall:
Labels from Experts (sometimes that is us)
Time-will-tell can produce training labels (eg, patients who responsed well to drug X)
“The crowd” (eg, Amazon Turk, hobbyists annotating sky images) can label
What might we do with unlabeled data?
Tells us where future data will lie in feature space
We might want algorithms to group/cluster data
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

3. Clustering (given: points w/o labels)
Feature Space
: : : … : : . …. : :
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

4. Algorithm: K-Means Clustering
Choose K, the number of clusters
Randomly choose K examples, let these be the initial cluster centers
Repeat until nothing changes (or out of time)
Assign each example to the closest cluster center
Replace each cluster center with the centroid of its members (eg, use the mean value of each feature)
If we want to ‘label’ NEW data, assign to nearest cluster center
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

5. Some On-Line Visualizations
Very short video (13 seconds)
Short video (140 seconds)
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

6. Hierarchical Clustering
Find two (unpaired) most similar examples, create parent (pseudo) example using average values
Repeat until one ‘super’ example left
Only ‘look at’ top portion of result (depending on how many clusters one wants to inspect)
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

7. Hierarchical Clustering
If we want FOUR clusters
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

8. Expectation-Maximization (EM; Dempster et al. 1977)
Find the (locally) optimal settings for some parameters
Often used to find the most likely values for some missing feature values
E-step (reasoning)
given a model, predict the most likely parameter values
M-step (learning)
given parameter values, learn a new model (then go to E-step unless new model same as old model)
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

9. cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14
EM: One Concrete Usage
Assume our missing parameter is the LABEL for all our examples (ie, unsupervised learning)
Initially guess labels for each example
Train Naïve Bayes using these now labeled examples
Use the Naïve Bayes model to predict the labels for all the (originally) unlabeled ex’s
If any labels changed, go to Step 2
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

10. Worked Example (assume K=2, ie two categories)
Initial RANDOM
Assignments
New
Assignments (use Model 1 to predict)
A B C T F ?
Model 1 (use m=1 and 2 more T ex’s and 2 more F ex’s)
p(A|T)= p(A|F)=0.33
p(B|T)= p(B|F)=0.67
p(C|T)= p(C|F)= p(T)= p(F)=0.38
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

11. Worked Example (assume K=2, ie two categories)
Initial RANDOM
Assignments
New
Assignments (use Model 1 to predict)
A B C T F T F
In this tiny data set, after the first round the predictions stabilized, so done
But in a larger dataset, likely to be some label changes the first few rounds
Model 1 (use m=1 and 2 more T ex’s and 2 more F ex’s)
p(A|T)= p(A|F)=0.33
p(B|T)= p(B|F)=0.67
p(C|T)= p(C|F)= p(T)= p(F)=0.38
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

12. Worked Example TWO (assume K=2, ie two categories)
Initial RANDOM
Assignments
New
Assignments (use Model 1b to predict)
A B C ? ?
Model 1b (use m=1 and 2 more T ex’s and 2 more F ex’s)
p(A|T)= p(A|F)=
p(B|T)= p(B|F)=
p(C|T)= p(C|F)= p(T)= p(F)=
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

13. cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14
Some Notes on EM via NB
Could have BOTH labeled and unlabelled data – called semi-supervised ML
Could use same idea to fill in missing feature values (common use of EM)
use that feature as the category to predict using the other features (might do this separately for POS and NEG examples)
More mathematical treatments of EM:
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

14. Recall: Auto-Association
Train ANN to Predict the Input Another unsupervised method 1 1
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cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14

15. Wrapup on Unlabeled Data
Lots of unlabeled data; want to make good use of it
Can cluster it
Can augment the labeled data (semi-supervised ML)
A weakness of many algo’s that use unlabeled data is user must select the number of clusters
Many equally good ways to cluster unlabeled data, so how to judge which is best?
Before performing clustering, make sure you don’t really have a SUPERVISED ML task!
12/8/16
cs540 - Fall 2015 (Shavlik©), Lecture 25, Week 14