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Probabilistic Linear Discriminant Analysis

Demo with MNIST Handwritten Digits Data

If you installed this package in a virtual environment, remember to activate that virtual environment first. Link: mnist_demo/mnist_demo.ipynb.


If you are new to programming, research, or sharing remote machines, you will save yourself a lot of headache by installing the following software: git and conda.

Easy install.

You can make a new conda environment called myenv with both this package and its python dependencies automatically installed with the following steps.

  1. cd into your favorite directory.
  2. git clone
  3. conda env create -f plda/environment.yml -n myenv

Installing with pip install.

  1. cd into your favorite directory.
  2. git clone
  3. If you have one, activate your virtual environment.
  4. Run either pip install plda/ or pip install ./plda.

Installing using your own conda environment.yml file.

  1. Add the following to the end of your dependencies. Here is an example: environment.yml.
- python>=3.5
- numpy~=1.14.2
- scipy~=1.0.1
- scikit-learn~=0.19.1
- pip=20.2.1
- pip:
  - git+git://

Testing the software

See tests/

Credit and disclaimers

Paper Citation

Ioffe S. (2006) Probabilistic Linear Discriminant Analysis. In: Leonardis A., Bischof H., Pinz A. (eds) Computer Vision – ECCV 2006. ECCV 2006.

More thanks!

@seandickert and @matiaslindgren pushed for and implemented the same-different discrimination and the pip install, respectively!


  1. Parameters are estimated via empirical Bayes.
  2. I wrote this code while working on an Explainable Artificial Intelligence (XAI) project at the CoDaS Laboratory, so it keeps parameters in memory that are unnecessary for simple classification problems. It's intended to be readable to researchers.


Probabilistic Linear Discriminant Analysis & classification, written in Python.




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