Information Retrieval in High
Dimensional Data
Wintersemester 2011213
Prof. Dr. M. Kleinsteuber and Dipl. Math. M. Seibert,
Geometric Optimization and Machine Learning Group,
TU München
Information Retrieval in High Dimensional Data
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A test: Find this person in the audience:
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How do we extract/store the picture‘s information?
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Where would you go for a 12 months stay? Analyze the following data:
Dataset 1
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Where to go for a 12months stay? Analyze the following data:
Dataset 2
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Where to go for a 12months stay? Analyze the following data:
Dataset 3
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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?
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More examples
Speech Recognition
Text Classification
Image Analysis
Recognize digits/faces
Sound Separation
Data Visualization
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In this course:
Get in touch with some of the tools!
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 2002.
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INSTEAD: Outline of the course:
1.
Curse of Dimensionality
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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
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Literature:
J. Izenman. Modern Multivariate Statistical Techniques.
Springer 2008.
J.A. Lee, M. Verleysen: Nonlinear Dimensionality Reduction,
Springer 2007.
T. Hastie, R. Tibshirani, J. Friedman. The elements of
statistical Learning, Springer 2009.
Papers (will be provided when appropriate)
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Data Analysis
Books/Papers/Internet...
Communicate Contents
Give feedback/Ask questions
mk
Studis
 Accept Methods
 Be interested
 Be independent
 Ask questions
 Give feedback
mk
 Choose methods
 Choose topics
 Address the questions
 Accept Feedback
Studis
Structure of Course
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)
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Questions?
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