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Applying inverse stereographic projection to manifold learning and clustering

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Abstract

In machine learning, a data set is often viewed as a point set distributed on a manifold. Using Euclidean norms to measure the proximity of this data set reduces the efficiency of learning methods. Also, many algorithms like Laplacian Eigenmaps or spectral clustering that require to measure similarity assume the k-Nearest Neighbors of any point are quite equal to the local neighborhood of the point on the manifold using Euclidean norms. In this paper, we propose a new method that intelligently transforms data on an unknown manifold to an n-sphere by the conformal stereographic projection, which preserves the angles and similarities of data in the original manifold. Therefore similarities represent actual similarities of the data in the original space. Experimental results on various problems, including clustering and manifold learning, show the effectiveness of our method.

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Notes

  1. https://scikit-learn.org/stable/datasets/index.html

  2. https://scikit-learn.org/stable/datasets/index.html

  3. https://scikit-learn.org/stable/datasets/index.html

  4. http://vision.ucsd.edu/content/yale-face-database

  5. http://featureselection.asu.edu/old/datasets.php

  6. https://scikit-learn.org/0.19/datasets/mldata.html

  7. https://jundongl.github.io/scikit-feature/datasets.html

  8. https://scikit-learn.org/stable/datasets/index.html

  9. http://www.cad.zju.edu.cn/home/dengcai/Data/MLData.html

  10. https://github.com/2232088201/Python-sparse-subspace-clustering-ADMM

  11. https://github.com/mitscha

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Authors and Affiliations

  1. Department of Mathematics, Tarbiat Modares University, Tehran, Iran

    Kajal Eybpoosh & Abbas Heydari

  2. Department of Computer Science, Tarbiat Modares University, Tehran, Iran

    Mansoor Rezghi

Authors
  1. Kajal Eybpoosh
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  2. Mansoor Rezghi
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  3. Abbas Heydari
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Correspondence to Mansoor Rezghi.

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Eybpoosh, K., Rezghi, M. & Heydari, A. Applying inverse stereographic projection to manifold learning and clustering. Appl Intell 52, 4443–4457 (2022). https://doi.org/10.1007/s10489-021-02513-0

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