Information Retrieval in High Dimensional Data 1 Wintersemester 2011213 Prof. Dr. M. Kleinsteuber and Dipl. Math. M. Seibert, Geometric Optimization and.

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Presentation transcript:

Information Retrieval in High Dimensional Data 1 Wintersemester Prof. Dr. M. Kleinsteuber and Dipl. Math. M. Seibert, Geometric Optimization and Machine Learning Group, TU München

A test: Find this person in the audience: Information Retrieval in High Dimensional Data 2

How do we extract/store the picture‘s information? 3 Information Retrieval in High Dimensional Data

4 Where would you go for a 12 months stay? Analyze the following data: Dataset 1 Information Retrieval in High Dimensional Data

5 Where to go for a 12months stay? Analyze the following data: Dataset 2 Information Retrieval in High Dimensional Data

6 Where to go for a 12months stay? Analyze the following data: Dataset 3 Information Retrieval in High Dimensional Data

Dataset 1 (Porto) Dataset 2 (Honululu) Dataset 3 (Canberra) How do we extract information? Is it possible to divide simply into „good“ and „bad“ climate? Is it possible to visualize climate-relatedness of cities? 7 Information Retrieval in High Dimensional Data

More examples 8 Information Retrieval in High Dimensional Data Speech Recognition Text Classification Image Analysis  Recognize digits/faces Sound Separation Data Visualization

In this course: 9 Information Retrieval in High Dimensional Data No Support Vector Machines No Regression No Factor Analysis No Random Projection No Neural Networks No Hidden Markov Models No Bayes Classifier No Self Organizing Maps..... Reference: I. Fodor: A survey of dimension reduction techniques, Technical Report, Berkeley Get in touch with some of the tools!

INSTEAD: Outline of the course: 1. Curse of Dimensionality 2. Statistical Decision Making 3. Principal Component Analysis 4. Linear Discriminant Analysis 5. Independent Component Analysis 6. Multidimensional Scaling 7. Isomap vs. Local Linear Embedding 8. Christmas 9. Kernel PCA 10. Robust PCA 11. Sparsity and Morphological Component Analysis Computer Vision 10

Literature: J. Izenman. Modern Multivariate Statistical Techniques. Springer J.A. Lee, M. Verleysen: Nonlinear Dimensionality Reduction, Springer T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical Learning, Springer Papers (will be provided when appropriate) Information Retrieval in High Dimensional Data 11

GOAL Choosing Contents Data Analysis Books/Papers/Internet... mkStudis Communicate Contents Give feedback/Ask questions work indepently

Studismk  Accept Methods  Be interested  Be independent  Ask questions  Give feedback  Choose methods  Choose topics  Address the questions  Accept Feedback Have fun!

Structure of Course Information Retrieval in High Dimensional Data 14 Lecture 2 + Tutorials 2 (M. Seibert and I) (4 assignments+1 Poster Session) LABCOURSE (Matlab Programming/Discussion and reading group/Postersession/etc.) 3 Examination: assignments required (max. 5 x 20 pts) 33% 30 mins oral examination 66% (up to two persons per exam)

Questions? Information Retrieval in High Dimensional Data 15