Information Retrieval in High Dimensional Data
Download
1 / 15

Information Retrieval in High Dimensional Data Wintersemester 2011213 - PowerPoint PPT Presentation


  • 52 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Information Retrieval in High Dimensional Data Wintersemester 2011213' - hasad-pollard


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

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


A test: Find this person in the audience:

Information Retrieval in High Dimensional Data


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

Information Retrieval in High Dimensional Data


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

Dataset 1

Information Retrieval in High Dimensional Data


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

Dataset 2

Information Retrieval in High Dimensional Data


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

Dataset 3

Information Retrieval in High Dimensional Data


Dataset 1 (Porto) data:

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?

Information Retrieval in High Dimensional Data


More examples
More examples data:

Information Retrieval in High Dimensional Data

Speech Recognition

Text Classification

Image Analysis Recognize digits/faces

Sound Separation

Data Visualization


In this course
In this course: data:

Get in touch with some of the tools!

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.


Instead outline of the course
INSTEAD: Outline of the course: data:

Curse of Dimensionality

Statistical Decision Making

Principal Component Analysis

Linear Discriminant Analysis

Independent Component Analysis

Multidimensional Scaling

Isomap vs. Local Linear Embedding

Christmas

Kernel PCA

Robust PCA

Sparsity and Morphological Component Analysis

Computer Vision


Literature
Literature: data:

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


Data Analysis data:

Books/Papers/Internet...

workindepently

ChoosingContents

GOAL

CommunicateContents

Givefeedback/Askquestions

mk

Studis


Have data:fun!

  • Accept Methods

  • Be interested

  • Be independent

  • Ask questions

  • Give feedback

  • Choose methods

  • Choose topics

  • Address the questions

  • Accept Feedback

mk

Studis


Structure of course
Structure of Course data:

Information Retrieval in High Dimensional Data

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? data:

Information Retrieval in High Dimensional Data


ad