1. Project reminder Deadline: Monday 16. 5. 11:00 Prepare 10 minutes long pesentation (in Czech/Slovak), which you’ll present on Wednesday 18. 5. 2011 during the Data mining lecture/exercise.

2. Self-Organizing Map (SOM)

3. Unsupervised neural networks, equivalent to clustering. Two layers – input and output – The input layer represents the input variables. – The output layer: neurons arranged in a single line (one-dimensional) or a two-dimensional grid. Main feature – weights

4. Learning means adopting the weights. Each output receives inputs through the weights.  weight vector has the same dimensionality as the input vector The output of each neuron is its activation – weighted sum of inputs (i.e. linear activation function). 2 w 11 w 21 u = x 1 w 11 + x 2 w 21

5. The objective of learning: project high- dimensional data onto 1D or 2D output neurons. Each neuron incrementally learns to represent a cluster of data. The weights are adjusted incrementally – the weights of neurons are called codebook vectors (codebooks).

6. Competitive learning The so-called competitive learning (winner- takes-all). Competitive learning will be demonstrated on simple 1D network with two inputs. Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

7. First, number of output neurons (i.e. clusters) must be selected. – Not always known, do reasonable estimate, it is better to use more, not used can be eliminated later. Then initialize weights. – e.g. small random values – Or randomly choose some input vectors and use their values for the weights. Then competitive learning can begin.

8. The activation for each output neuron is calculated as weighted sum of inputs. E.g. for the output neuron 1, its activation u 1 = w 11 x 1 + w 21 x 2. Generally Activation is the dot product between input vector x and weight vector w j. Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

9. Dot product is not only, but also If |x| = |w j | = 1, then u j = cosθ. The closer these two vectors are (i.e. the smaller θ is), the bigger the u j is (cos 0 = 1). x w θ

10. Say it again, and loudly: The closer the weight and input vectors are, the bigger the neuron activation is. Dan na Hrad. A simple measure of the closeness – Euclidean distance between x and w j.

11. Scale the input vector so that its length is equal to one. |x|=1 An input is presented to the network. Scale weight vectors of individual output neurons to the unit length. |w|=1 Calculate, how close is input vector x to each of weight vector w j (j is 1 … # output neurons). The neuron which codebook is closest to the input vector becomes winner (BMU, Best Matching Unit). Its weights will be updated.

12. Weight update The weight vector w is updated so that it moves closer to the input x. x w d ΔwΔw β – learning rate

13. Recursive vs. batch learning Conceptually similar to online/batch learning Recursive learning: – update weights of the winning neuron after each presentation of input vector Batch learning: – the weight update for each input vector is noted – the average weight adjustment for each output neuron is done after the whole epoch When to terminate learning? – mean distance between neurons and inputs they represent is at a minimum – distance stops changing

14. Example Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

16. epoch Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

19. Topology is not preserved. Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

20. Meet today’s hero Teuvo Kohonen

21. Self-Organizing Maps SOM, also Self-Organizing Feature Map (SOFM), Kohonen neural network. Inspired by the function of brain: – Different brain regions correspond to specific aspects of human activities. – These regions are organized such that tasks of similar nature (e.g. speech and vision) are controlled by regions that are in spatial proximity each to other. – This is called topology preservation.

22. In SOM learning, not only the winner, but also the neighboring neurons adapt their weights. Neurons closer to the winner adjust weights more than farther neurons. Thus we need 1.to define the size of neighborhood 2.to define a way how much neighboring neurons adapt their weights

23. Neighborhood definition neighborhood radius r 1 2 3 1 2 Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006 1 1 2 2

24. Training in SOM Follows similar manner of standard winner-takes- all competitive training. However, new rule is used for weight changes. Suppose, that the BMU is at position {i win, j win } on the 2D map. Then all codebook vectors of BMU and neighbors are adjusted to w ’ j according to where NS is the neighbor strength varying with the distance to the BMU. β is learning rate.

25. Neighbor strength When using neighbor features, all neighbor codebooks are shifted towards the input vector. However, BMU updates most, and the farther away the neighbor neuron is, the less its weights update. The NS function tells us how the weight adjustment decays with distance from the winner.

27. Slide by Johan Everts

28. Linear Gaussian Exponential Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

29. 2D Side Effects source: http://www.cis.hut.fi/projects/somtoolbox/download/sc_shots2.shtml

30. Shrinking neighborhood size Large neighborhood – proper placement of neurons in the initial stage to broadly represent spatial organization of input data. Further refinement – subsequent shrinking of the neighborhood. The size of large starting neighborhood is reduced with iterations. σ 0 … initial neighborhood size σ t … neighborhood width at iteration t T … total number of iterations bringing neighborhood to zero (i.e. only winner) linear decayexponential decay

31. Learning rate decay The step length (learning rate β) is also reduce with iterations. Two common forms: linear or exponential decay Strategy: start with relatively high β, decrease gradually, but remain above 0.0 1 T … constant bringing β to zero (or small value) Weight update incorporating learning rate and neighborhood decay

32. Recursive/Batch Learning Batch mode, no neigborhood – equivalent to K-means Neighbor incorporating – topology preservation – Regions closer in input space are represented by neurons closer in the map.

33. Two Phases of SOM Training Two phases 1.ordering 2.convergence Ordering – neighborhood and learning rate are reduced to small values – topological ordering – start β high, gradually decrease, remain above 0.01 – neighborhood – cover whole output layer Convergence – fine tuning with the shrunk neighborhood – small non-zero (~0.01) learning rate, NS no more than 1 st neighborghood

34. Example contd. Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

35. neighborhood drops to 0 after 3 iterations Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

36. After 3 iterations Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006 topology preservation takes effect very quickly Complete training Converged after 40 epochs. Epochs

37. Complete training All vectors have found cluster centers Except one Solution: add one more neuron 1 2 3 6 5 4 Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

38. 1 2 3 6 5 4 7

39. 2D output Play with http://www.neuroinformatik.ruhr-uni-bochum.de/VDM/research/gsn/DemoGNG/GNG.html Self-organizing map neighborhood size learning rate

41. A self-organizing feature map from a square source space to a square (grid) target space. Duda, Hart, Stork, Pattern Classification, 2000

42. Some initial (random) weights and the particular sequence of patterns (randomly chosen) lead to kinks in the map; even extensive further training does not eliminate the kink. In such cases, learning should be re-started with randomized weights and possibly a wider window function and slower decay in learning. Duda, Hart, Stork, Pattern Classification, 2000

43. 2D maps on Multidimensional data Iris data set – 150 patterns, 4 attributes, 3 classes (Set – 1, Vers – 2, Virg – 3) – more than 2 dimensions, so all data can not be vizualized in a meaningful way – SOM can be used not only to cluster input data, but also to exlpore the relationships between different attributes. SOM structure – 8x8, hexagonal, exp decay of learning rate β (β init = 0.5, T max = 20x150 = 3000), NS: Gaussian

44. What can be learned? petal length and width have similar structure to the class panel  low length correlates with low width and these relate to class Versicolor sepal width – very different pattern class panel – boundary between Virginica and Setosa – classes overlap setosa versicolor virginica Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

45. Since we have class labels, we can assess the classification accuracy of the map. So first we train the map using all 150 patterns. And then we present input patterns aindividually again and note the winning neuron. – The class to which the input belongs is the class associated with this BMU codebook vector (see previous slide, Class panel). – Only the winner decides classification.

46. Vers – 100% accuracy Set – 86% Virg – 88% Overall accuracy = 91.3% Vers – 100% accuracy Set – 90% Virg – 94% Overall accuracy = 94.7% Sandhya Samarasinghe, Neural Networks for Applied Sciences and Engineering, 2006

47. U-matrix Distance between the neighboring codebook vectors can highlight different cluster regions in the map and can be a useful visualization tool Two neurons: w 1 = {w 11, w 21, … w n1 }, w 2 = {w 12, w 22, … w n2 } Euclidean distance between them The average of the distance to the nearest neighbors – unified distance, U -matrix

48. The larger the distance between neurons, the larger the U value and more separated the clusters. The lighter the color, the larger the U value. Large distance between this cluster (Iris versicolor) and the middle cluster (Iris setosa). Large distances between codebook vectors indicate a sharp boundary between the clusters.

49. Surface graph The height represents the distance. 3 rd row – large height = separation Other two clusters are not separated.

50. Quantization error Measure of the distance between codebook vectors and inputs. If for input vector x the winner is w c, then distortion error e can be calculated as Comput e for all input vectors and get average – quantization error, average map distortion error E.

51. Iris quantization error High distortion error indicates areas where the codebook vector is relatively far from the inputs. Such information can be used to refine the map to obtain a more uniform distortion error measure if a more faithful reproduction of the input distribution from the map is desired.