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  1. 1. Learning from labelled and unlabeled data Semi-Supervised Learning Machine Learning – PDEEC 2008/2009 Filipe Tiago Alves de Magalhães 26-04-2010
  2. 2. Semi-Supervised Learning Supervised Semi-Supervised Unsupervised Learning Learning Learning Labbeled + unlabeled data discover patterns in the data The data have no that relate data attributes target attribute (unlabeled). with a target (class) attribute. Typically, plenty of unlabeled data available. We want to explore the These patterns are then data to find some intrinsic utilized to predict the structures in them. values of the target attribute in future Tries to improve the predictive data instances. power using both labelled and unlabeled data. (Expected to be better than using one alone) 2
  3. 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) - Email classification (SPAM or No-SPAM) - Speech annotation (400h for each hour of conversation) -… 3
  4. 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. 4
  5. 5. Semi-Supervised Learning Sometimes, it may not be so hard to label data… www.espgame.org 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 5
  6. 6. Semi-Supervised Self-Training of Object Detection Models Chuck Rosenberg Martial Hebert Henry Schneiderman Google, Inc. Carnegie Mellon University Carnegie Mellon University 7th IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) 2005 6
  7. 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 7
  8. 8. Semi-Supervised Self-Training of Object Detection Models Object detection 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. 8
  9. 9. 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 9
  10. 10. Semi-Supervised Self-Training of Object Detection Models Training Approaches EM approach 10
  11. 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. 11
  12. 12. Semi-Supervised Self-Training of Object Detection Models Training Approaches Alternative 12
  13. 13. Semi-Supervised Self-Training of Object Detection Models Detector Overview (Experimental Setup) 1. Subwindow is processed for lighting correction 2. Two-level wavelet transform is applied 3. Features are computed by vector quantizing groups of wavelet coefficients 4. 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 13
  14. 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 200-300 pixels high and Independent test set – 44 images 300-400 pixels wide 102 test examples 15000 negative examples Training examples – 24 x 16 pixels (rotated, scaled and cropped) 14
  15. 15. Semi-Supervised Self-Training of Object Detection Models Training (Experimental Setup) Training the model with fully labeled data consists of the following steps: 1. 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) 2. Compute the wavelet transform of the subimages 3. Quantize each group of wavelet coefficients and build a naïve Bayes model with respect to each group to discriminate between positive and negative examples 4. Adjust the naïve Bayes model using boosting, but maintaining a linear decision function, effectively performing gradient descent on the margin 5. Compute a ROC curve for the detector using a cross validation set 6. Choose a threshold for the linear function, based on the final performance desired 15
  16. 16. Semi-Supervised Self-Training of Object Detection Models Selection Metrics (Experimental Setup) Selection metric is crucial to the performance of the training 1. 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 2. 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) 16
  17. 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 17
  18. 18. Semi-Supervised Self-Training of Object Detection Models Selection Metrics (Experimental Setup) position MSE selection Detector 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. 18
  19. 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. 19
  20. 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. 20
  21. 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. 21
  22. 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 22
  23. 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? d i e n c c r r e e a a s s e e s s 23
  24. 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? 24
  25. 25. Semi-Supervised Self-Training of Object Detection Models Conclusions/Discussion 1. 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. 2. 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. 3. 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. 25
  26. 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. 26
  27. 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. 27
  28. 28. ZZZZZZZZZZZZZZ….. Still Awake??? 28
  29. 29. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Andrew B. Goldberg Xiaojin Zhu Computer Sciences Department Computer Sciences Department University of Wisconsin-Madison University of Wisconsin-Madison TextGraphs: HLT/NAACL Workshop on Graph-based Algorithms for Natural Language Processing 2006 29
  30. 30. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Sentiment Categorization ? ? ? 30
  31. 31. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Sentiment Categorization 31
  32. 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. 2005. 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 32
  33. 33. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 33
  34. 34. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 34
  35. 35. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 35
  36. 36. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 36
  37. 37. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 50% accuracy  37
  38. 38. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 38
  39. 39. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 100% accuracy  39
  40. 40. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Goal 40
  41. 41. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Approach 41
  42. 42. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Measuring Loss over the Graph 42
  43. 43. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 43
  44. 44. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 44
  45. 45. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 45
  46. 46. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 46
  47. 47. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 47
  48. 48. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization 48
  49. 49. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Minimization now is non- trivial 49
  50. 50. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Finding a Closed-Form Solution 50
  51. 51. Seeing stars when there aren’t many stars: Vector of f Vector of given labels yi Graph-based semi-supervised reviews and sentiment categorization values for for labelled learning for predicted labels for allFinding a Closed-Form Solution reviews unlabeled reviews Labelled Unlabeled C= 51
  52. 52. Seeing stars when there aren’t many stars: Graph Laplacian Graph-based semi-supervised learning for sentiment categorization matrix Finding a Closed-Form Solution Constant parameter 52
  53. 53. 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 53
  54. 54. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Finding a Closed-Form Solution 54
  55. 55. 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. 55
  56. 56. 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) 56
  57. 57. 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) 57
  58. 58. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Experiments Number of unlabeled α = ak + bk’ neighbours c = k/L Size of labelled set Number of labelled neighbours Optimal Values (through cross-validation) c = 0.2 α = 1.5 58
  59. 59. 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 59
  60. 60. 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 60
  61. 61. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization Results In each row, in 20 trial average green is the best unlabeled set result and any accuracy for each results that could not be distinguished author across from it with a paired different labelled t-test at the 0.05 set sizes and level. methods 61
  62. 62. 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. 62
  63. 63. Human Semi-Supervised Learning Q: Do humans also use semi-supervised learning? A: Apparently, yes! 63
  64. 64. 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 64
  65. 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 65 National Academy of Sciences. 2001. p. 4800-4804.
  66. 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, 2007. 18(3): p. 254-260. 66 Image taken from www.dalla.is
  67. 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… 67
  68. 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 01. 2005, 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. 2006. • Bo Pang and Lillian Lee. 2005. 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. 2001. p. 4800-4804. • Graf, E., et al., Can Infants Map Meaning to Newly Segmented Words?: Statistical Segmentation and Word Learning. Psychological Science, 2007. 18(3): p. 254-260. 68

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