On this website you will find some code I wrote during the last couple of years on various projects. Maybe you find something interesting.


Some small packages of mostly matlab source code. They should have a file 'sample_run.m' included where the use is demonstrated with a sample run of the algorithm. Some packages will require the spider toolbox, a free machine learning toolbox for matlab.
K-MEANS Clustering Fast code for k-means clustering based on the paper [Elk03]. This version avoids a lot of distance calculations by maintaining bounds for all distances. It is a lot faster than the naive standard implementation of kmeans. Furthermore at each iteration it produces the same output as the standard algorithm, so there is no need to use the old one any more. kmeans@mloss
EPCA Exponential Family PCA (EF-PCA or EPCA). EF-PCA generalizes Principal Componnent Analysis to the exponential family. The model is taken from [Col02]. Currently it includes only the case of Poisson distributed observed variables. download:epca
pLSA Probabilistic Latent Semantic Analysis. This implementation comes with both EM and the tempered EM version for Maximum Likelihood learning. Parts of it are written as mex files so the algorithm benefits from the sparse structure of the training data. This implementation follows the paper from [Hof99] and is based on the code from this ICCV short course.download:plsa
LDA Multiclass Fisher Linear Discriminant Analysis object for spider. download:lda
PoEdges Product of Edge-perts Image denoising. There is a specific website for this code please visist the PoEdges website for code, examples and images.
RAP The Rate adapting Poisson model for information retrieval and object recognition. This project has its own webpage with code and the datasets. Please visit the RAP website


[Col02] A Generalization of Principal Component Analysis to the Exponential Family, M. Collins, S. Dasgupta, R. E. Schapire, Advances in Neural Information Processing Systems 14
[Hof99] Probabilistic Latent Semantic Analysis, T.Hofmann, Proc. of Uncertainty in Artificial Intelligence, UAI'99
[Elk03] Using the Triangle Inequality to Accelerate k-Means, C.Elkan. In Proceedings of the Twentieth International Conference on Machine Learning (ICML'03), pp. 147-153.