1. Learning from labelled and unlabeled data
Semi-Supervised Learning
Machine Learning – PDEEC 2008/2009
Filipe Tiago Alves de Magalhães

2. Unsupervised Learning
Semi-Supervised Learning
Supervised Learning
Semi-Supervised
Learning
Unsupervised Learning
Labbeled + unlabeled data
discover patterns in the data
that relate data attributes
with a target (class) attribute.
The data have no
target attribute (unlabeled).
Typically, plenty of
unlabeled data
available.
We want to explore the
data to find some intrinsic
structures in them.
These patterns are then
utilized to predict the
values of the target
attribute in future
data instances.
Tries to improve the predictive power using both labelled and unlabeled data. (Expected to be better than using one alone)

3. Semi-Supervised Learning
Unlabeled data is easy to obtain
Labelled data can be difficult to obtain
- human annotation is boring
- may require experts
- may require special equipment
- very time-consuming
Examples:
Web page classification (billions of pages)
classification (SPAM or No-SPAM)
Speech annotation (400h for each hour of conversation) …

4. Semi-Supervised Learning
Semi-Supervised learning can be seen as an excellent way to improve the results
that we would get using exclusively supervised or non-supervised methods, for the
same scenario.
Although we (or specialists) do not need to spend such a big effort labelling data,
a great concern must be faced for the design of good models, feature extraction,
kernels definition.

5. Semi-Supervised Learning
Sometimes, it may not be so hard to label data…
Tries to guess the user’s gender
based on his/her choices.
After that, we tell if it was
right or wrong
Takes advantage of player’s intervention in order to
enrich the training of automatic learning algorithms

6. 7th IEEE Workshops on Application of Computer Vision (WACV/MOTION'05)
Semi-Supervised Self-Training of Object Detection Models
Chuck Rosenberg
Google, Inc.
Martial Hebert
Carnegie Mellon University
Henry Schneiderman
Carnegie Mellon University
7th IEEE Workshops on Application of Computer Vision (WACV/MOTION'05)
2005

7. Semi-Supervised Learning Self-Training
L = (Xi , Yi )
Set of labelled data
U = (Xi , ? )
Set of unlabeled data
Algorithm
Repeat
Train a classifier C with training data L
Classify data in U with C
Find a subset U’ of U with the most confident scores
L + U’  L
U – U’  U

8. Semi-Supervised Self-Training of Object Detection Models
Object detection based on its shape
- time-consuming
- exhaustive labelling (background, foreground, object, non-object)
Try to simplify the collection and preparation of training data
- combining data labelled in different ways
- labelling of each image region can take the form of a probability
distribution over labels (“weakly” labelled)
- e.g., is more likely that the object is present in the centre of the image
- e.g., a certain image has a high likelihood of containing the object, but
its position is unknown.

9. If Semi-Supervised Self-Training of Object Detection Models
Training Approaches
Generic detection algorithm for classification of a subwindow in an image as being part of
the “object” class or the “clutter/everything else” class If
X – image feature vectors
xi – data at a specific location in the image (i = {1, … ,n} indexes images locations)
Y – class
f – foreground
b – background
θf – parameters of the foreground model
θb – parameters of the background model

10. Semi-Supervised Self-Training of Object Detection Models
Training Approaches
EM approach

11. Semi-Supervised Self-Training of Object Detection Models
Training Approaches
EM approach
There are many reasons why EM may not perform well in a particular semi-supervised training context.
EM solely finds a set of model parameters which maximize the likelihood of the data.
- Fully labeled data may not sufficiently constrain the solution, which means that there may be solutions which maximize the data likelihood but do not optimize classification performance.

12. Semi-Supervised Self-Training of Object Detection Models
Training Approaches
Alternative

13. Semi-Supervised Self-Training of Object Detection Models
Detector Overview (Experimental Setup)
Subwindow is processed for lighting correction
Two-level wavelet transform is applied
Features are computed by vector quantizing groups of wavelet coefficients
Subwindow is classified by thresholding a linear combination of the log-likelihood ratios of the features
Cascade architecture → only image patches which are accepted by the first detector are passed on to the next

14. Semi-Supervised Self-Training of Object Detection Models
Data (Experimental Setup)
Landmark used on a typical training image
sample training images and the training examples associated with them
Set with positive examples – 231 images
480 training examples
Independent test set – 44 images
102 test examples
15000 negative examples
Training examples – 24 x 16 pixels (rotated, scaled and cropped)
pixels high and pixels wide

15. Semi-Supervised Self-Training of Object Detection Models
Training (Experimental Setup)
Training the model with fully labeled data consists of the following steps:
Given the training data landmark locations
geometrically normalize the training example subimages;
apply lighting normalization to the subimages;
generate synthetic training examples (scaling, shifting and rotating)
Compute the wavelet transform of the subimages
Quantize each group of wavelet coefficients and build a naïve Bayes model with respect to each group to discriminate between positive and negative examples
Adjust the naïve Bayes model using boosting, but maintaining a linear decision function, effectively performing gradient descent on the margin
Compute a ROC curve for the detector using a cross validation set
Choose a threshold for the linear function, based on the final performance desired

16. Semi-Supervised Self-Training of Object Detection Models
Selection Metrics (Experimental Setup)
Selection metric is crucial to the performance of the training
Confidence selection
Computed at every iteration by applying the detector trained from the current set of labelled data to the weakly labelled data set.
Detection with highest confidence is selected and added to the training set
MSE selection
Is calculated for each weakly labelled example by evaluating the distance between the corresponding image window and all of the other templates in the training data (including the original labelled examples and the weakly labelled examples added in prior iterations)

17. Semi-Supervised Self-Training of Object Detection Models
Selection Metrics (Experimental Setup)
The candidate image and the labeled images are first normalized with a specific set of processing steps before the MSE based score metric is computed.
The score is based on the Mahalanobis distance

18. Semi-Supervised Self-Training of Object Detection Models
Selection Metrics (Experimental Setup)
position
Detector
MSE selection metric
scale
The detector must be accurate in localization but need not be accurate in detection since false detection will be discarded due to their large MSE distances to all of the training examples.
This is crucial to ensure the performance of the training algorithm with small initial training sets.
This is also part of the reason for the MSE to outperform the confidence
metric, which requires the detector to be accurate in both localization and detection performance.

19. Semi-Supervised Self-Training of Object Detection Models
Experiment Scenarios (Experiments and Analysis)
Each experiment was repeated using a different initial random subset, in order to avoid the variance that was being observed in the detector performance and in the behaviour of the semi-supervised training process.
Experiment = specific set of experimental conditions
Run = each repetition of that experiment
Mostly, 5 runs were performed for each experiment
Typically, 20 weakly labelled images were added to the training set at each iteration, because of the substantial training time of the detector.
Ideally, only a single image would be added at each iteration.

20. Semi-Supervised Self-Training of Object Detection Models
Evaluation Metrics (Experiments and Analysis)
Each run was evaluated by using the area under the ROC curve (AUC).
Because different experimental conditions affect performance, the AUCs were normalized relatively to the full data performance of that run.
if (performance level = = 1.0) {
the model being evaluated has the same performance
as it would if all of the labelled data was utilised }
if (performance level < 1.0)
the model has a lower performance
than that achieved with the full data set
To compute the full data performance, each specific run is trained with the full data set and its performance is recorded.

21. Semi-Supervised Self-Training of Object Detection Models
Baseline training configurations (Experiments and Analysis)
Smooth regime was chosen in order to perform experiments under conditions
where the addition of weakly labelled data would make a difference.

22. Semi-Supervised Self-Training of Object Detection Models
Selection Metrics (Experiments and Analysis)
Does the choice of the selection metric make a substantial difference in the performance of the semi-supervised training?
Confidence metric
MSE metric

23. Semi-Supervised Self-Training of Object Detection Models
Selection Metrics (Experiments and Analysis)
Does the choice of the selection metric make a substantial difference in the performance of the semi-supervised training?
decreases
increases

24. Semi-Supervised Self-Training of Object Detection Models
Relative size of fully Labelled Data(Experiments and Analysis)
How many weakly labelled examples do we need to add to the training set in order to reach the best detector performance?

25. Semi-Supervised Self-Training of Object Detection Models
Conclusions/Discussion
The results showed that it was possible to achieve detection performance that was close to the base performance obtained with the fully labelled data, even when a small fraction of the training data was used in the initial training set.
The experiments showed that the self-training approach to semi-supervised training can be applied to an existing detector that was originally designed for supervised training.
The MSE selection metric consistently outperformed the confidence metric. More generally, the self-training approach using an independently-defined selection metric outperforms both the confidence metrics and the batch EM approaches.
During the training process, the distribution of the labeled data at any particular iteration may not match the actual underlying distribution of the data.

26. Semi-Supervised Self-Training of Object Detection Models
Conclusions/Discussion
True labels for the unlabeled data
Original unlabeled data and labelled data
(c),(d) The points labelled by the incremental self-training algorithm after 5 iterations using the confidence metric and the Euclidean metric, respectively.

27. Semi-Supervised Self-Training of Object Detection Models
Future Work
Study the relation between the semi-supervised training approach evaluated here with the co-training approaches.
Develop more precise guidelines for selecting the initial training set.
The approach could be extended to training examples that are labelled in different
ways. For example, some images may be provided with scale information and nothing else. Additional information may be provided such as the rough shape of the object, or a prior distribution over its location in the image.

28. ZZZZZZZZZZZZZZ…..
Still Awake???

29. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Andrew B. Goldberg
Computer Sciences Department
University of Wisconsin-Madison
Xiaojin Zhu
Computer Sciences Department
University of Wisconsin-Madison
TextGraphs: HLT/NAACL Workshop on Graph-based Algorithms for Natural Language Processing
2006

30. ? ? ? Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Sentiment Categorization ? ? ?

31. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Sentiment Categorization

32. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
What we saw is rating inference
Bo Pang and Lillian Lee Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales. In Proceedings of the ACL.
In this work…
Graph-based Semi-supervised Learning
Main assumption encoded in graph:
Similar documents should have similar ratings

33. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

34. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

35. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

36. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

37. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
50% accuracy 

38. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

39. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
100% accuracy 

40. Goal Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Goal

41. Approach Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Approach

42. Measuring Loss over the Graph
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Measuring Loss over the Graph

43. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

44. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

45. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

46. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

47. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

48. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization

49. Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Minimization now
is non- trivial

50. Finding a Closed-Form Solution
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Finding a Closed-Form Solution
Fortunately, we

51. Finding a Closed-Form Solution
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Vector of f values for
all reviews
Vector of given labels yi for labelled reviews and predicted labels for unlabeled reviews
Finding a Closed-Form Solution
C =
Labelled
Unlabeled

52. Graph Laplacian matrix
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Graph Laplacian matrix
Finding a Closed-Form Solution
Constant parameter

53. Graph Laplacian Matrix
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Graph Laplacian Matrix
Assume n labelled and unlabeled documents

54. Finding a Closed-Form Solution
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Finding a Closed-Form Solution

55. Experiments Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Experiments
Predict 1 to 4 stars ratings for reviews
4-author data (Pang and Lee, 2005)
1770, 902, 1307 and 1027 documents, respectively
Each document represented as a {0,1} word-presence vector, normalized to sum 1
Positive-Sentence Percentage (PSP) similarity (Pang and Lee, 2005)
Tuned parameters with cross-validation *
* Joachims, T., Transductive Inference for Text Classification using Support Vector Machines, in Proceedings of the Sixteenth International Conference on Machine Learning. 1999, Morgan Kaufmann Publishers Inc.

56. Experiments Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Experiments
PSPi is defined as the percentage of positive sentences in review xi.
The similarity between reviews xi, xj is the cosine angle between the vectors
(PSPi,1-PSPi) and (PSPj, 1-PSPj)
Positive sentences are identified using a binary classifier trained on a “snippet
data set” (10662 documents)

57. Experiments Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Experiments
Low ratings tend to get low PSP scores
High ratings tend to get high PSP scores
The trend was qualitatively the same as in Pang and Lee (2005) (Naïve Bayes)

58. α = ak + bk’ c = k/L Experiments
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Experiments
Number of unlabeled neighbours
α = ak + bk’
c = k/L
Size of labelled set
Number of labelled neighbours
Optimal Values (through cross-validation)
c = 0.2
α = 1.5

59. outperforms other methods for small labelled set sizes
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Results
Graph-based SSL
outperforms other methods
for small labelled set sizes

60. Alternative Similarity Measure
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Alternative Similarity Measure
The cosine between word vectors containing all words,
each weighted by its mutual information
Scaling of mutual information values (maximum = 1)
Previously found values → weights for corresponding words in the word vectors
Words in the movie review data that did not appear in the “snippet data set” were excluded
Optimal Values (through cross-validation)
c = 0.1
α = 1.5

61. Results Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Results
In each row, in green is the best result and any results that could not be distinguished from it with a paired t-test at the 0.05 level.
20 trial average unlabeled set accuracy for each author across different labelled set sizes and methods

62. Conclusions and Future Work
Seeing stars when there aren’t many stars:
Graph-based semi-supervised learning for sentiment categorization
Conclusions and Future Work
Graph-based semi-supervised learning based on PSP similarity achieved better performance than all other methods in all four author corpora.
However, for larger labelled sets its performance was not so good.
a) Maybe, because SVM regressor trained on a large labelled set can achieve fairly high __accuracy without considering relationships between examples.
b) PSP similarity is not accurate enough, thus biasing the overall performance when labelled __data is abundant.
Investigate better document representations and similarity measures.
Extend the method to inductive learning setting
Experiment cross-reviewer and cross-domain analysis, such as using a model learned on movie reviews to help classify product reviews.

63. Human Semi-Supervised Learning
Q: Do humans also use semi-supervised learning?
A: Apparently, yes!

64. Hint: Temporal association
Human Semi-Supervised Learning
Some evidences…
Face recognition is a very challenging computational task.
However, it is an easy task for humans.
Differences between two views of the same face are much larger than those between two different faces viewed at the same angle. +
+ Sinha, P., et al., Face recognition by humans: 20 results all computer vision researchers should know about. 2006, MIT.
Hint: Temporal association

65. Human Semi-Supervised Learning
Some evidences…
Observers were shown sequences of novel faces in which the identity of the face changed as the head rotated.
image sequence
Unlabeled data
As a result, observers showed a tendency to
treat the views as if they were of the same person.
suggests
We are continuously associating views of objects to support later recognition, and that we do so not only on the basis of the physical similarity, but also the correlated appearance in time of the objects.
Wallis, G. and H. Bülthoff, Effects of temporal association on recognition memory, in National Academy of Sciences p

66. Human Semi-Supervised Learning
Some evidences…
17-month infants listen to a word, see an object
They wanted to measure their ability to associate the word and the object
If the word was heard many times before (without seeing the object;
unlabeled data), association was stronger.
If the word was not heard before, association was weaker.
Graf, E., et al., Can Infants Map Meaning to Newly Segmented Words?: Statistical Segmentation and Word Learning. Psychological Science, (3): p
Image taken from

67. Human Semi-Supervised Learning
Better understanding of the human cognitive model,
can guide the development of better machine learning
algorithms or make existent even better and robust…

68. References
Rosenberg, C., M. Hebert, and H. Schneiderman, Semi-Supervised Self-Training of Object Detection Models, in Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume , IEEE Computer Society.
Goldberg, A.B. and X. Zhu. Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization. in TextGraphs: HLT/NAACL Workshop on Graph-based Algorithms for Natural Language Processing
Bo Pang and Lillian Lee Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales. In Proceedings of the ACL.
Joachims, T., Transductive Inference for Text Classification using Support Vector Machines, in Proceedings of the Sixteenth International Conference on Machine Learning. 1999, Morgan Kaufmann Publishers Inc.
Sinha, P., et al., Face recognition by humans: 20 results all computer vision researchers should know about. 2006, MIT.
Wallis, G. and H. Bülthoff, Effects of temporal association on recognition memory, in National Academy of Sciences p
Graf, E., et al., Can Infants Map Meaning to Newly Segmented Words?: Statistical Segmentation and Word Learning. Psychological Science, (3): p