Kernel Regression

1. Kernel Regression Prof. Bennett Math Model of Learning and Discovery 2/24/03 Based on Chapter 2 of Shawe-Taylor and Cristianini

2. Outline • Review Ridge Regression • LS-SVM=KRR • Dual Derivation • Bias Issue • Summary

3. Ridge Regression Review • Use least norm solution for fixed • Regularized problem • Optimality Condition: Requires 0(n3) operations

4. Dual Representation • Inverse always exists for any • Alternative representation: Solving ll equation is 0(l3)

5. Dual Ridge Regression • To predict new point: • Note need only compute G, the Gram Matrix Ridge Regression requires only inner products between data points

6. Linear Regression in Feature Space Key Idea: Map data to higher dimensional space (feature space) and perform linear regression in embedded space. Embedding Map:

7. Kernel Function • A kernel is a function K such that • There are many possible kernels. Simplest is linear kernel.

8. Ridge Regression in Feature Space • To predict new point: • To compute the Gram Matrix Use kernel to compute inner product

9. Alternative Dual Derivation • Original math model • Equivalent math model • Construct dual using Wolfe Duality

10. Lagrangian Function • Consider the problem • Lagrangian function is

11. Wolfe Dual Problem • Primal • Dual

12. Lagrangian Function • Primal • Lagrangian

13. Wolfe Dual Problem Construct Wolfe Dual Simplify by eliminating z=

14. Simplified Problem Get rid of z Simplify by eliminating w=X’

15. Simplified Problem Get rid of w

16. Optimal solution • Problem in matrix notation with G=XX’ • Solution satisfies

17. What about Bias • If we limit regression function to f(x)=w’x means that solution must pass through origin. • Many models may require a bias or constant factor f(x)=w’x+b

18. Eliminate Bias • One way to eliminate bias is to “center” the data Make data have mean of 0

19. Center y Y now has sample mean of 0 Frequently good to make y have standard length:

20. Center X • Mean X • Center X

21. You Try • Consider data matrix with 3 points in 4 dimensions • Computer the centered by hand and with the following formula.

22. Center (X) in Feature Space • We cannot center (X) directly in feature space. • Center G = XX’ • Works in feature space too for G in kernel space

23. Centering Kernel Practical Computation:

24. Ridge Regression in Feature Space • Original way • Predicted normalized y • Predicted original y

25. Worksheet • Normalized Y • Invert to get unnormalized y

26. Centering Test Data Calculate test data just like training data: Prediction of test data becomes:

27. Alternate Approach • Directly add bias to the model: • Optimization problem becomes:

28. Lagrangian Function • Consider the problem • Lagrangian function is

29. Lagrangian Function • Primal

30. Wolfe Dual Problem Simplify by eliminating z= and using e’ =0

31. Simplified Problem Simplify by eliminating w=X’

32. Simplified Problem Get rid of w

33. New Problem to be solved • Problem in matrix notation with G=XX’ • This is a constrained optimization problem. Solution is also system of equations, but not as simple.

34. Kernel Ridge Regression • Centered algorithm just requires centering of the kernel and solving one equation. • Can also add bias directly. • + Lots of fast equation solvers. • + Theory supports generalization • - requires full training kernel to compute  • - requires full training kernel to predict future points