Dissimilarity Representation Course
This page collects the material for a 4-day course on the dissimilarity representation using the Matlab toolboxes DisTools and PRTools. (There is also a very short 2-hour set of examples). It is assumed that students are familiar with the concepts of pattern recognition and/or machine learning. The exercises described here are intended as an illustration of oral lectures and offer hand-on experiments by which a better understanding is gained. Moreover, the may serve as a start for further research and building applications.
Background material
Preparation
- Download PRTools, DisTools and PRDisData.
- Unzip and add them to your personal Matlab directory
- Start Matlab
- Change to your Matlab directory using the cd command
- Add the above toolboxes to the Matlab path by (copy and paste)
addpath(fullfile(pwd,'prtools')))
addpath(fullfile(pwd,'distools'))
addpath(fullfile(pwd,'prdisdata')
- Check the availability of PRTools, DisTools and PRDisData by
which ldc
which pe_em
which chickenpieces
They should all respond with a proper path.
Day 1: Introduction PRTools
- Introduction PRTools examples
- Scatterplots
- Datasets
- Datafiles
- Mappings
- Classifiers
- Evaluation
- Crossvalidation
- Learning curves
- Feature curves
- Dimension reduction
- Combining classifiers
- Dissimilarities
Day 2: Dissimilarity space
- Classifiers in dissimilarity space
- Dimension reduction
- Combining dissimilarity matrices
- Generalized dissimilarities
- Semi-supervised learning
Day 3: Embedding
Day 4: Applications
