# PPT

AI and Robotics

Oct 19, 2013 (3 years and 8 months ago)

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Information Retrieval in High Dimensional Data

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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

A test: Find this person in the audience:

Information Retrieval in High Dimensional Data

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How do we extract/store the picture‘s information?

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Information Retrieval in High Dimensional Data

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Where would you go for a 12 months stay? Analyze the following data:

Dataset 1

Information Retrieval in High Dimensional Data

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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

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?

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Information Retrieval in High Dimensional Data

More examples

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Information Retrieval in High Dimensional Data

Speech Recognition

Text Classification

Image Analysis

Recognize digits/faces

Sound Separation

Data Visualization

In this course:

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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 2002.

Get in touch with some of the tools!

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Curse of Dimensionality

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Statistical Decision Making

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Principal Component Analysis

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Linear Discriminant Analysis

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Independent Component Analysis

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Multidimensional Scaling

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Isomap vs. Local Linear Embedding

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Christmas

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Kernel PCA

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Robust PCA

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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)

Information Retrieval in High Dimensional Data

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Data Analysis

Books/Papers/Internet...

mk

Studis

Communicate

Contents

Give

questions

Studis

mk

Accept Methods

Be interested

Be independent

Give feedback

Choose methods

Choose topics

Accept Feedback

Structure of Course

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Lecture 2 + Tutorials 2 (M. Seibert and I) (4 assignments+1
Poster Session)

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

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