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# Low Rank Approximation Techniques Project ( ❛ ͜ʖ ❛ )
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Mini projet du cours Low Rank Approximation Techniques. \
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PDF des références:
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A project for the course Low-rank Approximation Techniques.
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![le chat](img4_100.jpg?raw=true "Title") + ![le Big Ben](img2_100.jpg?raw=true "Title") = ![le chat dans le Big Ben](img42.jpg?raw=true "Title")
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## Main references:
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- [1] [Massively scalable Sinkhorn distances via the Nyström method](https://arxiv.org/pdf/1812.05189.pdf)
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- [2] [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf)
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- [3] [Adaptive color transfer with relaxed optimal transport](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7025983)
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Prendre des images de 100 par 100
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On peut afficher nos images dans le README mdr:
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![le chat](img4_100.jpg?raw=true "Title") + ![le Big Ben](img2_100.jpg?raw=true "Title") = ![le chat dans le Big Ben](img42.jpg?raw=true "Title")
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## Code:
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- The notebook [main.ipynb](main.ipynb) contain all runnable code and output all the figures. Change the number of the images in the main to chose which original images you want to use.

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