1. Direct Kernel Methods

2. Data mining is the process of automatically extracting valid, novel, potentially useful and ultimately comprehensible information from very large databases

3. Direct Kernel Methods database data prospecting and surveying selected data select transformed data preprocess & transform make model Interpretation& rule formulation The Data Mining Process

4. How is Data Mining Different? Emphasis on large data sets - Not all data fit in memory (necessarily) - Outlier detection, rare events, errors, missing data, minority classes - Scaling of computation time with data size is an issue - Large data sets: i.e., large number of records and/or large number of attributes fusion of databases Emphasis on finding interesting, novel non-obvious information - It is not necessarily known what exactly one is looking for - Models can be highly nonlinear - Information nuggets can be valuable Different methods - Statistics - Association rules & Pattern recognition - AI - Computational intelligence (neural nets, genetic algorithms, fuzzy logic) - Support vector machines and kernel-based methods - Visualization (SOM, pharmaplots) Emphasis on explaining and feedback Interdisciplinary nature of data mining

5. Direct Kernel Methods Data Mining Challenges Large data sets - Data sets can be rich in the number of data - Data sets can be rich in the number of attributes Data preprocessing and feature definition - Data representation - Attribute/Feature selection - Transforms and scaling Scientific data mining - Classification, multiple classes, regression - Continuous and binary attributes - Large datasets - Nonlinear Problems Erroneous data, outliers, novelty, and rare events - Erroneous data - Outliers - Rare events - Novelty detection Smart visualization techniques Feature Selection & Rule formulation

6. Direct Kernel Methods UNDERSTANDING WISDOM DATA INFORMATION KNOWLEDGE

7. Direct Kernel Methods A Brief History in Data Mining: Pascal  Bayes  Fisher  Werbos  Vapnik The meaning of “Data Mining” changed over time: - Pre 1993: “Data mining is art of torturing the data into a confession” - Post 1993: “Data mining is the art of charming the data into confession” From the supermarket scanner to the human genome - Pre 1998: Database marketing and marketing driven applications - Post 1998: The emergence of scientific data mining From AI expert systems  data-driven expert systems: - Pre 1990: The experts speak (AI Systems) - Post 1995: Attempts to let the data to speak for themselves - 2000+: The data speak … A brief history of statistics and statistical learning theory: - From the calculus of chance to the calculus of probabilities (Pascal  Bayes) - From probabilities to statistics (Bayes  Fisher) - From statistics to machine learning (Fisher & Tuckey  Werbos  Vapnik) From theory to application

8. Data Preparation - Missing data - Data cleansing - Visualization - Data transformation Clustering/Classification Statistics Factor analysis/Feature selection Associations Regression models Data driven expert systems Meta-Visualization/Interpretation Database Marketing Finance Health Insurance Medicine Bioinformatics Manufacturing WWW Agents Text Retrieval Data Mining Applications and Operations “Homeland” “Security” BioDefense

9. Direct Kernel Methods Direct Kernel Methods for Data Mining: Outline Classical (linear) regression analysis and the learning paradox Resolving the learning paradox by - Resolving the rank deficiency (e.g., PCA) - Regularization (e.g., Ridge Regression) Linear and nonlinear kernels Direct kernel methods for nonlinear regression - Direct Kernel Principal Component Analysis  DK-PCA - (Direct) Kernel Ridge Regression  Least Squares SVM (LS-SVM) - Direct Kernel Partial Least Squares  Partial Least-Squares SVM - Direct Kernel Self-Organizing Maps  DK-SOM Feature selection, memory requirements, hyperparameter selection Examples: - Nonlinear toy examples (DK-PCA Haykin’s Spiral, LS-SVM for Cherkassky data) - K-PLS for Time series data - K-PLS for QSAR drug design - LS-SVM Nerve agent classification with electronic nose - K-PLS with feature selection on microarray gene expression data (leukemia) - Direct Kernel SOM and DK-PLS for Magnetocardiogram data - Direct Kernel SOM for substance identification from spectrograms

10. Direct Kernel Methods Outline Classical (linear) regression analysis and the learning paradox Resolving the learning paradox by - Resolving the rank deficiency (e.g., PCA) - Regularization (e.g., Ridge Regression) Linear and nonlinear kernels Direct kernel methods for nonlinear regression - Direct Kernel Principal Component Analysis  DK-PCA - (Direct) Kernel Ridge Regression  Least Squares SVMs (LS-SVM) - Direct Kernel Partial Least Squares  Partial Least-Squares SVMs - Direct Kernel Self-Organizing Maps  DK-SOM Feature selection, memory requirements, hyperparameter selection Examples: - Nonlinear toy examples (DK-PCA Haykin’s Spiral, LS-SVM for Cherkassky data) - K-PLS for Time series data - K-PLS for QSAR drug design - LS-SVM Nerve agent classification with electronic nose - K-PLS with feature selection on microarray gene expression data (leukemia) - Direct Kernel SOM and DK-PLS for Magnetocardiogram data

11. Direct Kernel Methods Review: What is in a Kernel? A kernel can be considered as a (nonlinear) data transformation - Many different choices for the kernel are possible - The Radial Basis Function (RBF) or Gaussian kernel is an effective nonlinear kernel The RBF or Gaussian kernel is a symmetric matrix - Entries reflect nonlinear similarities amongst data descriptions - As defined by:

12. Docking Ligands is a Nonlinear Problem

13. Direct Kernel Methods Surface properties are encoded on 0.002 e/au 3 surface Breneman, C.M. and Rhem, M. [1997] J. Comp. Chem., Vol. 18 (2), p. 182-197 Histograms or wavelet encoded of surface properties give Breneman’s TAE property descriptors 10x16 wavelet descriptore Electron Density-Derived TAE-Wavelet Descriptors PIP (Local Ionization Potential) Histograms Wavelet Coefficients

14. Direct Kernel Methods Binding affinities to human serum albumin (HSA): log K’hsa Gonzalo Colmenarejo, GalaxoSmithKline J. Med. Chem. 2001, 44, 4370-4378 95 molecules, 250-1500+ descriptors 84 training, 10 testing (1 left out) 551 Wavelet + PEST + MOE descriptors Widely different compounds Acknowledgements: Sean Ekins (Concurrent) N. Sukumar (Rensselaer)

15. Direct Kernel Methods Validation Model: 100x leave 10% out validations

16. Direct Kernel Methods PLS, K-PLS, SVM, ANN Feature Selection (data strip mining)

17. Direct Kernel Methods 511 features 32 features K-PLS Pharmaplots

18. Direct Kernel Methods Microarray Gene Expression Data for Detecting Leukemia 38 data for training 36 data for testing Challenge: select ~10 out of 6000 genes used sensitivity analysis for feature selection (with Kristin Bennett)

19. Direct Kernel Methods

21. with Wunmi Osadik and Walker Land (Binghamton University) Acknowledgement: NSF

22. Direct Kernel Methods Magnetocardiography at CardioMag Imaging inc.

23. Direct Kernel Methods Left: Filtered and averaged temporal MCG traces for one cardiac cycle in 36 channels (the 6x6 grid). Right Upper: Spatial map of the cardiac magnetic field, generated at an instant within the ST interval. Right Lower: T3-T4 sub-cycle in one MCG signal trace

24. Direct Kernel Methods Magneto-cardiogram Data with Karsten Sternickel (Cardiomag Inc.) and Boleslaw Szymanski (Rensselaer) Acknowledgemnent: NSF SBIR phase I project

25. Direct Kernel Methods SVMLib Linear PCA Direct Kernel PLS SVMLib

26. Direct Kernel Methods Direct Kernel PLS with 3 Latent Variables

27. Direct Kernel Methods Direct Kernel with Robert Bress and Thanakorn Naenna

28. GATCAATGAGGTGGACACCAGAGGCGGGGACTTGTAAATAACACTGGGCTGTAGGAGTGA TGGGGTTCACCTCTAATTCTAAGATGGCTAGATAATGCATCTTTCAGGGTTGTGCTTCTA TCTAGAAGGTAGAGCTGTGGTCGTTCAATAAAAGTCCTCAAGAGGTTGGTTAATACGCAT GTTTAATAGTACAGTATGGTGACTATAGTCAACAATAATTTATTGTACATTTTTAAATAG CTAGAAGAAAAGCATTGGGAAGTTTCCAACATGAAGAAAAGATAAATGGTCAAGGGAATG GATATCCTAATTACCCTGATTTGATCATTATGCATTATATACATGAATCAAAATATCACA CATACCTTCAAACTATGTACAAATATTATATACCAATAAAAAATCATCATCATCATCTCC ATCATCACCACCCTCCTCCTCATCACCACCAGCATCACCACCATCATCACCACCACCATC ATCACCACCACCACTGCCATCATCATCACCACCACTGTGCCATCATCATCACCACCACTG TCATTATCACCACCACCATCATCACCAACACCACTGCCATCGTCATCACCACCACTGTCA TTATCACCACCACCATCACCAACATCACCACCACCATTATCACCACCATCAACACCACCA CCCCCATCATCATCATCACTACTACCATCATTACCAGCACCACCACCACTATCACCACCA CCACCACAATCACCATCACCACTATCATCAACATCATCACTACCACCATCACCAACACCA CCATCATTATCACCACCACCACCATCACCAACATCACCACCATCATCATCACCACCATCA CCAAGACCATCATCATCACCATCACCACCAACATCACCACCATCACCAACACCACCATCA CCACCACCACCACCATCATCACCACCACCACCATCATCATCACCACCACCGCCATCATCA TCGCCACCACCATGACCACCACCATCACAACCATCACCACCATCACAACCACCATCATCA CTATCGCTATCACCACCATCACCATTACCACCACCATTACTACAACCATGACCATCACCA CCATCACCACCACCATCACAACGATCACCATCACAGCCACCATCATCACCACCACCACCA CCACCATCACCATCAAACCATCGGCATTATTATTTTTTTAGAATTTTGTTGGGATTCAGT ATCTGCCAAGATACCCATTCTTAAAACATGAAAAAGCAGCTGACCCTCCTGTGGCCCCCT TTTTGGGCAGTCATTGCAGGACCTCATCCCCAAGCAGCAGCTCTGGTGGCATACAGGCAA CCCACCACCAAGGTAGAGGGTAATTGAGCAGAAAAGCCACTTCCTCCAGCAGTTCCCTGT GATCAATGAGGTGGACACCAGAGGCGGGGACTTGTAAATAACACTGGGCTGTAGGAGTGA TGGGGTTCACCTCTAATTCTAAGATGGCTAGATAATGCATCTTTCAGGGTTGTGCTTCTA TCTAGAAGGTAGAGCTGTGGTCGTTCAATAAAAGTCCTCAAGAGGTTGGTTAATACGCAT GTTTAATAGTACAGTATGGTGACTATAGTCAACAATAATTTATTGTACATTTTTAAATAG CTAGAAGAAAAGCATTGGGAAGTTTCCAACATGAAGAAAAGATAAATGGTCAAGGGAATG GATATCCTAATTACCCTGATTTGATCATTATGCATTATATACATGAATCAAAATATCACA CATACCTTCAAACTATGTACAAATATTATATACCAATAAAAAATCATCATCATCATCTCC ATCATCACCACCCTCCTCCTCATCACCACCAGCATCACCACCATCATCACCACCACCATC ATCACCACCACCACTGCCATCATCATCACCACCACTGTGCCATCATCATCACCACCACTG TCATTATCACCACCACCATCATCACCAACACCACTGCCATCGTCATCACCACCACTGTCA TTATCACCACCACCATCACCAACATCACCACCACCATTATCACCACCATCAACACCACCA CCCCCATCATCATCATCACTACTACCATCATTACCAGCACCACCACCACTATCACCACCA CCACCACAATCACCATCACCACTATCATCAACATCATCACTACCACCATCACCAACACCA CCATCATTATCACCACCACCACCATCACCAACATCACCACCATCATCATCACCACCATCA CCAAGACCATCATCATCACCATCACCACCAACATCACCACCATCACCAACACCACCATCA CCACCACCACCACCATCATCACCACCACCACCATCATCATCACCACCACCGCCATCATCA TCGCCACCACCATGACCACCACCATCACAACCATCACCACCATCACAACCACCATCATCA CTATCGCTATCACCACCATCACCATTACCACCACCATTACTACAACCATGACCATCACCA CCATCACCACCACCATCACAACGATCACCATCACAGCCACCATCATCACCACCACCACCA CCACCATCACCATCAAACCATCGGCATTATTATTTTTTTAGAATTTTGTTGGGATTCAGT ATCTGCCAAGATACCCATTCTTAAAACATGAAAAAGCAGCTGACCCTCCTGTGGCCCCCT TTTTGGGCAGTCATTGCAGGACCTCATCCCCAAGCAGCAGCTCTGGTGGCATACAGGCAA CCCACCACCAAGGTAGAGGGTAATTGAGCAGAAAAGCCACTTCCTCCAGCAGTTCCCTGT WORK IN PROGRESS

29. Direct Kernel Methods Santa Fe Time Series Prediction Competition 1994 Santa Fe Institute Competition: 1000 data chaotic laser data, predict next 100 data Competition is described in Time Series Prediction: Forecasting the Future and Understanding the Past, A. S. Weigend & N. A. Gershenfeld, eds., Addison-Wesley, 1994 Method: - K-PLS with  = 3 and 24 latent variables - Used records with 40 past data for training for next point - Predictions bootstrap on each other for 100 real test data Entry “would have won” the competition

30. Direct Kernel Methods www.drugmining.com Kristin Bennett and Mark Embrechts