This page is under construction. In the meantime you can:
Download the subspace clustering / segmentation **MATLAB** code that is presented in the paper
Vasileios Zografos, Liam Ellis, Rudolf Mester **Discriminative Subspace Clustering**. CVPR 2013
Get the code here
You will also need **PRtools** for the quadratic classifier (will remove this dependency in the near future).
Get a local copy of PRtools here

**Instructions: **
Unzip "DiSC.zip" a single folder. Unzip; "prtools.zip" anywhere you like and add a path to it in Matlab.
Then, given a data matrix W=[D,N] where D=number of ambient dimensions and N=number of points, simply run
[I,I_ALL,ProjectionMatrix]=DiSC(W,dim,final_clusters,'quadrc',lambda,Ensembles);
where:
**Input**
- dim = max dimension of the subspaces
- final_clusters = the number of clusters (i.e. subspaces)
- lambda = regularisation parameter based on noise. Try 0.01
- Ensembles = the number of classifiers for the ensemble. Try 50

**Output**
- I = the label vector of the clustering (that is the final answer)
- I_ALL= the label vector for each of the classifiers in the ensemble)
- ProjectionMatrix = the random projection matrix used for each ensemble classifier

**Some results: **
**The MNIST dataset **
**The YaleB dataset (including background) **
**Synthetic subspace dataset (To be released soon) **
Back to homepage