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Online Multiple Kernel Classification Steven C.H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang Machine Learning (2013) Presented by Audrey Cheong Electrical.

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Presentation on theme: "Online Multiple Kernel Classification Steven C.H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang Machine Learning (2013) Presented by Audrey Cheong Electrical."— Presentation transcript:

1 Online Multiple Kernel Classification Steven C.H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang Machine Learning (2013) Presented by Audrey Cheong Electrical & Computer Engineering MATH 6397: Data Mining

2 Online Multiple Kernel Classification (OMKC) Background - Online Online learning Learns one instance at a time and predicts labels for future instances Learner is given an instance Learner predicts the label of the instance Learner is given the correct label Learner refines its prediction mechanism 2

3 Online Multiple Kernel Classification (OMKC) Background – Multiple Kernel Composed of two online learning algorithms: Perceptron algorithm (Rosenblatt 1958) Type of linear classifier Learns a classifier for a given kernel Hedge algorithm (Freund and Schapire 1997) Combines classifiers by linear weights Perceptron Hedge 3

4 Online Multiple Kernel Classification (OMKC) Perceptron algorithm 4

5 Online Multiple Kernel Classification (OMKC) Hedge algorithm 5

6 Online Multiple Kernel Classification (OMKC) Notations 6

7 Online Multiple Kernel Classification (OMKC) Proposed framework 7

8 Online Multiple Kernel Classification (OMKC) Algorithms Deterministic approach: all kernels are used Stochastic approach: a subset of kernels are used 8 Deterministic Stochastic Deterministic Stochastic Update Combination

9 Online Multiple Kernel Classification (OMKC) OMKC (D,D) 9 … Kernel classifiers : Prediction: Combined Prediction: … Deterministic update Deterministic combination Deterministic Stochastic Deterministic Stochastic Update Combination

10 Online Multiple Kernel Classification (OMKC) OMKC (S,S) 10 … Kernel classifiers : Prediction: Combined Prediction: … Stochastic update Deterministic Stochastic Deterministic Stochastic Update Combination Stochastic combination

11 Online Multiple Kernel Classification (OMKC) Experimental setup 11 binary datasets

12 Online Multiple Kernel Classification (OMKC) Experimental setup 12

13 Online Multiple Kernel Classification (OMKC) Evaluation of the deterministic OMKC algorithm Comparison of the deterministic OMKC algorithm with three Perceptron based algorithms Perceptron : the well-known Perceptron baseline algorithm with a linear kernel (Rosenblatt 1958; Freund and Schapire 1999) Perceptron(u) : another Perceptron baseline algorithm with an unbiased/uniform combination of all the kernels Perceptron(*): an online validation procedure to search for the best kernel among the pool of kernels (using the first 10 % training examples), and then apply the Perceptron algorithm with the best kernel OM-2: a state-of-the-art online learning algorithm for multiple kernel learning (Jie et al. 2010; Orabona et al. 2010) 13

14 Online Multiple Kernel Classification (OMKC) Evaluation of the deterministic OMKC algorithm 14 < > <

15 Online Multiple Kernel Classification (OMKC) Average mistake rate (20 runs) 15

16 Online Multiple Kernel Classification (OMKC) Number of support vectors (20 runs) 16

17 Online Multiple Kernel Classification (OMKC) Kernel weights 17

18 Online Multiple Kernel Classification (OMKC) 18

19 Online Multiple Kernel Classification (OMKC) Time Efficiency 19 Decreases as size increases

20 Online Multiple Kernel Classification (OMKC) Conclusion All the OMKC algorithms usually perform better than the regular Perceptron algorithm with an unbiased linear combination of multiple kernels the Perceptron algorithm with the best kernel found by validation the state-of-the-art online MKL algorithm The deterministic combination strategy usually performs better Stochastic updating strategy improves computational efficiency without decreasing the accuracy significantly 20

21 21 1)How many kernel classifiers were used in the stochastic combination? 2)How was the number of support vectors determined? Should the support vectors be given in terms of the number of support vectors per kernel classifier? Did support vectors overlap between kernel classifiers?

22 Online Multiple Kernel Classification (OMKC) References Hoi, S. C. H., Jin, R., Zhao, P., & Yang, T. (2012). Online Multiple Kernel Classification. Machine Learning, 90(2), 289–316. doi:10.1007/s10994-012-5319-2 22

23 Online Multiple Kernel Classification (OMKC) Algorithm 1 All kernels are used 23 Deterministic Stochastic Deterministic Stochastic Update Combination Normalize the weights Update Combination

24 Online Multiple Kernel Classification (OMKC) Algorithm 1 → 2 24 Stochastic combination Deterministic update 17: Update Combination Deterministic Stochastic Deterministic Stochastic

25 Online Multiple Kernel Classification (OMKC) Algorithm 2 → 3 25 Deterministic Stochastic Deterministic Stochastic Update Combination

26 Online Multiple Kernel Classification (OMKC) Algorithm 2 → 3 26 Deterministic Stochastic Deterministic Stochastic Deterministic combination Stochastic update Update Combination

27 Online Multiple Kernel Classification (OMKC) Algorithm 4 27 Deterministic Stochastic Deterministic Stochastic Stochastic update Stochastic combination Update Combination

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