Object Orie’d Data Analysis, Last Time

1. Object Orie’d Data Analysis, Last Time • Classical Discrimination (aka Classification) • FLD & GLR very attractive • MD never better, sometimes worse • HDLSS Discrimination • FLD & GLR fall apart • MD much better • Maximal Data Piling • HDLSS space is a strange place

2. Kernel Embedding Aizerman, Braverman and Rozoner (1964) • Motivating idea: Extend scope of linear discrimination, By adding nonlinear components to data (embedding in a higher dim’al space) • Better use of name: nonlinear discrimination?

3. Kernel Embedding Stronger effects for higher order polynomial embedding: E.g. for cubic, linear separation can give 4 parts (or fewer)

4. Kernel Embedding General View: for original data matrix: add rows: i.e. embed in Then Higher slice Dimensional with a Space hyperplane

5. Kernel Embedding EmbeddedFisher Linear Discrimination: Choose Class 1, for any when: in embedded space. • image of class boundaries in original space is nonlinear • allows more complicated class regions • Can also do Gaussian Lik. Rat. (or others) • Compute image by classifying points from original space

6. Kernel Embedding Visualization for Toy Examples: • Have Linear Disc. In Embedded Space • Study Effect in Original Data Space • Via Implied Nonlinear Regions Approach: • Use Test Set in Original Space (dense equally spaced grid) • Apply embedded discrimination Rule • Color Using the Result

7. Kernel Embedding Polynomial Embedding, Toy Example 1: Parallel Clouds

8. Kernel Embedding Polynomial Embedding, Toy Example 1: Parallel Clouds • PC 1: • always bad • finds “embedded greatest var.” only) • FLD: • stays good • GLR: • OK discrimination at data • but overfitting problems

9. Kernel Embedding Polynomial Embedding, Toy Example 2: Split X

10. Kernel Embedding Polynomial Embedding, Toy Example 2: Split X • FLD: • Rapidly improves with higher degree • GLR: • Always good • but never ellipse around blues…

11. Kernel Embedding Polynomial Embedding, Toy Example 3: Donut

12. Kernel Embedding Polynomial Embedding, Toy Example 3: Donut • FLD: • Poor fit for low degree • then good • no overfit • GLR: • Best with No Embed, • Square shape for overfitting?

13. Kernel Embedding Drawbacks to polynomial embedding: • too many extra terms create spurious structure • i.e. have “overfitting” • HDLSS problems typically get worse

14. Kernel Embedding Hot Topic Variation: “Kernel Machines” Idea: replace polynomials by other nonlinear functions e.g. 1: sigmoid functions from neural nets e.g. 2: radial basis functions Gaussian kernels Related to “kernel density estimation” (recall: smoothed histogram)

15. Kernel Embedding Radial Basis Functions: Note: there are several ways to embed: • Naïve Embedding (equally spaced grid) • Explicit Embedding (evaluate at data) • Implicit Emdedding (inner prod. based) (everybody currently does the latter)

16. Kernel Embedding Naïve Embedding, Radial basis functions: At some “grid points” , For a “bandwidth” (i.e. standard dev’n) , Consider ( dim’al) functions: Replace data matrix with:

17. Kernel Embedding Naïve Embedding, Radial basis functions: For discrimination: Work in radial basis space, With new data vector , represented by:

18. Kernel Embedding Naïve Embedd’g, Toy E.g. 1: Parallel Clouds • Good at data • Poor outside

19. Kernel Embedding Naïve Embedd’g, Toy E.g. 2: Split X • OK at data • Strange outside

20. Kernel Embedding Naïve Embedd’g, Toy E.g. 3: Donut • Mostly good • Slight mistake for one kernel

21. Kernel Embedding Naïve Embedding, Radial basis functions: Toy Example, Main lessons: • Generally good in regions with data, • Unpredictable where data are sparse

22. Kernel Embedding Toy Example 4: Checkerboard Very Challenging! Linear Method? Polynomial Embedding?

23. Kernel Embedding Toy Example 4: Checkerboard Polynomial Embedding: • Very poor for linear • Slightly better for higher degrees • Overall very poor • Polynomials don’t have needed flexibility

24. Kernel Embedding Toy Example 4: Checkerboard Radial Basis Embedding + FLD Is Excellent!

25. Kernel Embedding Drawbacks to naïve embedding: • Equally spaced grid too big in high d • Not computationally tractable (gd) Approach: • Evaluate only at data points • Not on full grid • But where data live

26. Kernel Embedding Other types of embedding: • Explicit • Implicit Will be studied soon, after introduction to Support Vector Machines…

27. Kernel Embedding generalizations of this idea to other types of analysis & some clever computational ideas. E.g. “Kernel based, nonlinear Principal Components Analysis” Ref: Schölkopf, Smola and Müller (1998)

28. Support Vector Machines Motivation: • Find a linear method that “works well” for embedded data • Note: Embedded data are very non-Gaussian • Suggests value of really new approach

29. Support Vector Machines Classical References: • Vapnik (1982) • Boser, Guyon & Vapnik (1992) • Vapnik (1995) Excellent Web Resource: • http://www.kernel-machines.org/

30. Support Vector Machines Recommended tutorial: • Burges (1998) Recommended Monographs: • Cristianini & Shawe-Taylor (2000) • Schölkopf & Alex Smola (2002)

31. Support Vector Machines Graphical View, using Toy Example: • Find separating plane • To maximize distances from data to plane • In particular smallest distance • Data points closest are called support vectors • Gap between is called margin

32. SVMs, Optimization Viewpoint Formulate Optimization problem, based on: • Data (feature) vectors • Class Labels • Normal Vector • Location (determines intercept) • Residuals (right side) • Residuals (wrong side) • Solve (convex problem) by quadratic programming

33. SVMs, Optimization Viewpoint Lagrange Multipliers primal formulation (separable case): • Minimize: Where are Lagrange multipliers Dual Lagrangian version: • Maximize: Get classification function:

34. SVMs, Computation Major Computational Point: • Classifier only depends on data through inner products! • Thus enough to only store inner products • Creates big savings in optimization • Especially for HDLSS data • But also creates variations in kernel embedding (interpretation?!?) • This is almost always done in practice

35. SVMs, Comput’n & Embedding For an “Embedding Map”, e.g. Explicit Embedding: Maximize: Get classification function: • Straightforward application of embedding • But loses inner product advantage

36. SVMs, Comput’n & Embedding Implicit Embedding: Maximize: Get classification function: • Still defined only via inner products • Retains optimization advantage • Thus used very commonly • Comparison to explicit embedding? • Which is “better”???

37. SVMs & Robustness Usually not severely affected by outliers, But a possible weakness: Can have very influential points Toy E.g., only 2 points drive SVM Notes: • Huge range of chosen hyperplanes • But all are “pretty good discriminators” • Only happens when whole range is OK??? • Good or bad?

38. SVMs & Robustness Effect of violators (toy example): • Depends on distance to plane • Weak for violators nearby • Strong as they move away • Can have major impact on plane • Also depends on tuning parameter C

39. SVMs, Computation Caution: available algorithms are not created equal Toy Example: • Gunn’s Matlab code • Todd’s Matlab code Serious errors in Gunn’s version, does not find real optimum…

40. SVMs, Tuning Parameter Recall Regularization Parameter C: • Controls penalty for violation • I.e. lying on wrong side of plane • Appears in slack variables • Affects performance of SVM Toy Example: d = 50, Spherical Gaussian data

41. SVMs, Tuning Parameter Toy Example: d = 50, Spherical Gaussian data X=Axis: Opt. Dir’n Other: SVM Dir’n • Small C: • Where is the margin? • Small angle to optimal (generalizable) • Large C: • More data piling • Larger angle (less generalizable) • Bigger gap (but maybe not better???) • Between: Very small range

42. SVMs, Tuning Parameter Toy Example: d = 50, Spherical Gaussian data Careful look at small C: Put MD on horizontal axis E.g. • Shows SVM and MD same for C small • Mathematics behind this? • Separates for large C • No data piling for MD

43. Distance Weighted Discrim’n Improvement of SVM for HDLSS Data Toy e.g. (similar to earlier movie)

44. Distance Weighted Discrim’n Toy e.g.: Maximal Data Piling Direction - Perfect Separation - Gross Overfitting - Large Angle - Poor Gen’ability

45. Distance Weighted Discrim’n Toy e.g.: Support Vector Machine Direction - Bigger Gap - Smaller Angle - Better Gen’ability - Feels support vectors too strongly??? - Ugly subpops? - Improvement?

46. Distance Weighted Discrim’n Toy e.g.: Distance Weighted Discrimination - Addresses these issues - Smaller Angle - Better Gen’ability - Nice subpops - Replaces min dist. by avg. dist.

47. Distance Weighted Discrim’n Based on Optimization Problem: More precisely: Work in appropriate penalty for violations Optimization Method: Second Order Cone Programming • “Still convex” gen’n of quad’c program’g • Allows fast greedy solution • Can use available fast software (SDP3, Michael Todd, et al)

48. Distance Weighted Discrim’n 2=d Visualization: Pushes Plane Away From Data All Points Have Some Influence

49. DWD Batch and Source Adjustment • Recall from Class Meeting, 9/6/05: • For Perou’s Stanford Breast Cancer Data • Analysis in Benito, et al (2004) Bioinformatics • https://genome.unc.edu/pubsup/dwd/ • Use DWD as useful direction vector to: • Adjust for Source Effects • Different sources of mRNA • Adjust for Batch Effects • Arrays fabricated at different times

50. DWD Adj: Biological Class Colors & Symbols