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[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering implemented with Pytorch

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[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering with PyTorch and Matlab equivalent code.
Sample Images from PyTorch code

Input Data Fiedler Vector Clusters

Drawing the second eigenvector on data (diffusion map) Diffusion Map- Second Eigenvector on data
Drawing the point-wise diffusion distances Diffusion Map- point-wise distances

Sorting matrix

Unsorted PairWiseDistance Matrix Sorted Distance Matrix


Use with Pytorch for general purpose computations by implementing some very elegant methods for dimensionality reduction and graph spectral clustering.


In this repo, I am using PyTorch in order to implement various methods for dimensionality reduction and spectral clustering. At the moment, I have added Diffusion Maps [1] and I am working on the methods presented in the following list (as well as some other that I will add in the future).

Except from some examples based on 2-D Gaussian distributed clusters I will also add examples with face, food, imagenet categories etc.


In order to run these examples you need to have Pytorch installed in your system. I worked with Anaconda2 and Pytorch:

pytorch                   0.2.0           py27hc03bea1_4cu80  [cuda80]  soumith

(you can verify your pytorch installation by running

conda list | grep pytorch

Feel free to contact me for suggestions, comments etc.


  • [1] Diffusion maps, RR Coifman, S Lafon, Applied and computational harmonic analysis 21 (1), 5-30
  • [2] Jianbo Shi and Jitendra Malik (1997): "Normalized Cuts and Image Segmentation", IEEE Conference on Computer Vision and Pattern Recognition, pp 731–737
  • [3] Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. 2001. On spectral clustering: analysis and an algorithm. In Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic (NIPS'01), T. G. Dietterich, S. Becker, and Z. Ghahramani (Eds.). MIT Press, Cambridge, MA, USA, 849-856.
  • [4] ...

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