We recently released the source code of a number of Sparse Coding algorithms under a common Python module called ProSper. ProSper contains contains a parallelised implementation of the Binary Sparse Coding (BSC) [1], Gaussian Sparse Coding (GSC) [2], Maximum Causes Analysis (MCA) [3], Maximum Magnitude Causes Analysis (MMCA) [4], Ternary Sparse Coding (TSC) [5], and Discrete Sparse Coding [7] models. All these probabilistic generative models are trained using a truncated Expectation Maximisation (EM) algorithm [6].

It is designed to have a uniform interface across algorithms as well as logging and reporting utilities. Therefore, it simplifies switching between the different Sparse Coding algorithms.

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[1] M. Henniges, G. Puertas, J. Bornschein, J. Eggert, and J. Lücke (2010). Binary Sparse Coding. Proc. LVA/ICA 2010, LNCS 6365, 450-457.

[2] A.-S. Sheikh, J. A. Shelton, J. Lücke (2014). A Truncated EM Approach for Spike-and-Slab Sparse Coding. Journal of Machine Learning Research, 15:2653-2687.

[3] G. Puertas, J. Bornschein, and J. Lücke (2010). The Maximal Causes of Natural Scenes are Edge Filters. Advances in Neural Information Processing Systems 23, 1939-1947.

[4] J. Bornschein, M. Henniges, J. Lücke (2013). Are V1 simple cells optimized for visual occlusions? A comparative study. PLOS Computational Biology 9(6): e1003062.

[5] G. Exarchakis, M. Henniges, J. Eggert, and J. Lücke (2012). Ternary Sparse Coding. International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 204-212.

[6] J. Lücke and J. Eggert (2010). Expectation Truncation and the Benefits of Preselection in Training Generative Models. Journal of Machine Learning Research 11:2855-2900.

[7] G. Exarchakis, and J. Lücke (2017). Discrete Sparse Coding. Neural Computation, 29(11), 2979-3013.