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Computational Audio-Vision Lab

Copyright © University of Tehran Computational Audio-Vision Lab 2015

http://visionlab.ut.ac.ir/

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Computational Audio-Vision Lab | visionlab.ut.ac.ir Reviews

https://visionlab.ut.ac.ir

Copyright © University of Tehran Computational Audio-Vision Lab 2015

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Projects | Computational Audio-Vision Lab

http://visionlab.ut.ac.ir/projects.html

A mixture-model estimation toolbox for MATLAB. Using MixEst, you can fit arbitrary distributions (usually mixture models) to your data set by various optimization techniques, with minimal effort. The toolbox architecture is based on defining all the distributions in a compatible way, such that each distribution exposes a common interface for calculating things such as the log-likelihood or entropy. School of Electrical and Computer Engineering,. College of Engineering, University of Tehran.

2

Resources | Computational Audio-Vision Lab

http://visionlab.ut.ac.ir/resources.html

Exploiting generalization in the subspaces for faster model-based learning. Expected Logarithm of Central Quadratic Form. Kmeans for Mixtures of Von Mises-Fisher Distributions. Improved Bayesian Information Criterion. School of Electrical and Computer Engineering,. College of Engineering, University of Tehran.

3

People | Computational Audio-Vision Lab

http://visionlab.ut.ac.ir/people.html

Learning, Vision, Speech. Pourya H. Zadeh. Learning on PSD matrices. School of Electrical and Computer Engineering,. College of Engineering, University of Tehran.

4

Publications | Computational Audio-Vision Lab

http://visionlab.ut.ac.ir/publications.html

S Sra, R. Hosseini. Geometric Optimization in Machine Learning. In Algorithmic Advances in Riemannian Geometry and Applications, Springer, 2016. P H Zadeh, R. Hosseini. Expected logarithm of central quadratic form and its use in KL-Divergence of some distributions. A Mehrjou, R. Hosseini, B.N. Araabi. Improved Bayesian information criterion for mixture model selection. Pattern Recognition Letters, 2016. R Hosseini, S. Sra, L. Theis, M. Bethge. Computational Statistics and Data Analysis, 2016. Advances in...

5

MixEst

http://visionlab.ut.ac.ir/mixest

MixEst is a MATLAB toolbox for mixture-model parameter estimation. Easy to Learn and Use. We have tried our best to design the toolbox in a way that the scripts are legible and self-explanatory. You can learn most of the functions by looking at the examples. No need to refer to the documentation for simple use cases. Free and Open Source. You can download and use our toolbox free of charge. Please cite our paper if you publish work using MixEst. Check out the docs.

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