Semi-Supervised Learning10701/slides/17_SSL.pdfSemi-supervised SVM Many others 9. Notation 10....
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Semi - Supervised Learning Barnabas Poczos Slides Courtesy: Jerry Zhu, Aarti Singh
Transcript of Semi-Supervised Learning10701/slides/17_SSL.pdfSemi-supervised SVM Many others 9. Notation 10....
Semi-Supervised Learning
Barnabas Poczos
Slides Courtesy: Jerry Zhu, Aarti Singh
Supervised Learning
Learning algorithm
Labeled
Goal:
Feature Space Label Space
Optimal predictor (Bayes Rule) depends on unknown PXY, so instead
learn a good prediction rule from training data
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Labeled and Unlabeled data
Human expert/
Special equipment/
Experiment
“Crystal” “Needle” “Empty”
Cheap and abundant ! Expensive and scarce !
“0” “1” “2” …
“Sports”
“News”
“Science”
…
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Free-of-cost labels?
Luis von Ahn: Games with a purpose (ReCaptcha)
Word challenging to OCR (Optical Character Recognition)
You provide a free label!
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Semi-Supervised learning
Supervised learning (SL)
Semi-Supervised learning (SSL)
Learning algorithm
Goal: Learn a better prediction rule than based on labeled data alone.
“Crystal”
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Semi-Supervised learning in Humans
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Can unlabeled data help?
Assume each class is a coherent group (e.g. Gaussian)
Then unlabeled data can help identify the boundary more accurately.
Positive labeled data
Negative labeled data
Unlabeled data
Supervised Decision Boundary
Semi-Supervised Decision Boundary
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Can unlabeled data help?
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“0” “1” “2” …
“Similar” data points have “similar” labels8
This embedding can be done by manifold learning algorithms
Some SSL Algorithms
▪ Self-Training
▪ Generative methods, mixture models
▪ Graph-based methods
▪ Co-Training
▪ Semi-supervised SVM
▪ Many others
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Notation
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Self-training
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Self-training Example
Propagating 1-NN
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Mixture Models for Labeled Data
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Mixture Models for Labeled Data
> 1/2<
Estimate the
parameters from the
labeled data
Decision for any test
point not in the labeled
dataset
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Mixture Models for Labeled Data
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Mixture Models for SSL Data
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Mixture Models
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Mixture ModelsSL vs SSL
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Mixture Models
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Gaussian Mixture Models
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EM for Gaussian Mixture Models
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Assumption for GMMs
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Assumption for GMMs
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Assumption for GMMs
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Related: Cluster and Label
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Graph Based Methods
Assumption: Similar unlabeled data have similar labels.
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Graph RegularizationSimilarity Graphs: Model local neighborhood relations between data points
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Assumption:
Nodes connected by heavy edges
tend to have similar label
Graph Regularization
If data points i and j are similar (i.e. weight wij is large), then their labels
are similar fi = fj
Loss on labeled data
(mean square,0-1)
Graph based smoothness prior
on labeled and unlabeled data
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Co-training
Co-training (Blum & Mitchell, 1998) (Mitchell, 1999) assumes that (i) features can be split into two sets; (ii) each sub-feature set is sufficient to train a good classifier.
• Initially two separate classifiers are trained with the labeled data, on the two sub-feature sets respectively.
• Each classifier then classifies the unlabeled data, and ‘teaches’ the other classifier with the few unlabeled examples (and the predicted labels) they feel most confident.
• Each classifier is retrained with the additional training examples given by the other classifier, and the process repeats.
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Co-training Algorithm
Co-training AlgorithmBlum & Mitchell’98
Semi-Supervised SVMs
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Semi-Supervised Learning
▪ Generative methods
▪ Graph-based methods
▪ Co-Training
▪ Semi-Supervised SVMs
▪ Many other methods
SSL algorithms can use unlabeled data to help improve prediction accuracy if data satisfies appropriate assumptions
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