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  1. docs/blog/avatars/ryan-johnson.jpg

    ryan-johnson.jpg...
    Image
    - Registered: 2020-04-10 12:36
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  2. examples/miscellaneous/plot_johnson_lindenstrauss_bound.py

    The Johnson-Lindenstrauss bound for embedding with random projections
    =====================================================================
    
    
    The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
    dataset can be randomly projected into a lower dimensional Euclidean
    space while controlling the distortion in the pairwise distances.
    
    .. _`Johnson-Lindenstrauss lemma`: https://en.wikipedia.org/wiki/\
    Python
    - Registered: 2020-07-03 09:24
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  3. examples/plot_johnson_lindenstrauss_bound.py

    The Johnson-Lindenstrauss bound for embedding with random projections
    =====================================================================
    
    
    The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
    dataset can be randomly projected into a lower dimensional Euclidean
    space while controlling the distortion in the pairwise distances.
    
    Python
    - Registered: 2020-04-23 20:57
    - Last Modified: 2019-09-14 21:40
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  4. machine_learning/dimensionality_reduction/a-sparse-johnson-lindenstrauss-transform.pdf

    rections.
    8. REFERENCES
    [1] D. Achlioptas. Database-friendly random projections:
    Johnson–Lindenstrauss with with binary coins. Journal of Computer
    and System Sciences, 66(4):671–687, 2003.
    [2] N. Ailon and B. Chazelle. The fast Johnson–Lindenstrauss transform
    and approximate nearest neighbors. SIAM Journal on Computing,
    39(1):302–322, 2009.
    PDF
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  5. sublinear_algorithms/An-Elementary-Proof-of-a-Theorem-of-Johnson-and-Lindenstrauss.pdf

    An Elementary Proof of a Theorem of
    Johnson and Lindenstrauss
    Sanjoy Dasgupta,1 Anupam Gupta2
    1AT&T Labs Research, Room A277, Florham Park, New Jersey 07932; e-mail:
    ******@****.***
    2Lucent Bell Labs, Room 2C-355, 600 Mountain Avenue, Murray Hill,
    New Jersey 07974; e-mail: ******@****.***
    Received 16 December 2001; accepted 11 July 2002
    DOI 10.1002/rsa.10073
    PDF
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  6. doc/modules/random_projection.rst

       245-250.
    
    
    .. _johnson_lindenstrauss:
    
    The Johnson-Lindenstrauss lemma
    ===============================
    
    The main theoretical result behind the efficiency of random projection is the
    `Johnson-Lindenstrauss lemma (quoting Wikipedia)
    <https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma>`_:
    
      In mathematics, the Johnson-Lindenstrauss lemma is a result
    Plain Text
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  7. machine_learning/README.md

    * [Support-Vector Networks](http://rd.springer.com/content/pdf/10.1007%2FBF00994018.pdf)
    
      The initial paper on support-vector networks for classification.
    
    * [The Fast Johnson-Lindenstrauss Transforms](https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf)
    
    Plain Text
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  8. sublinear_algorithms/README.md

    * :scroll: **[An Elementary Proof of a Theorem of Johnson and Lindenstrauss](https://github.com/papers-we-love/papers-we-love/blob/master/sublinear_algorithms/An-Elementary-Proof-of-a-Theorem-of-Johnson-and-Lindenstrauss.pdf)**
    
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  9. docs/_style/prism-master/tests/languages/asciidoc/attributes_feature.test

    [quote,'&#34;with *an* image&#34; image:foo.png[]...
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  10. examples/preprocessing/plot_map_data_to_normal.py

    """
    =================================
    Map data to a normal distribution
    =================================
    
    .. currentmodule:: sklearn.preprocessing
    
    This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms
    through :class:`~PowerTransformer` to map data from various
    distributions to a normal distribution.
    
    The power transform is useful as a transformation in modeling problems where
    Python
    - Registered: 2020-07-03 09:24
    - Last Modified: 2019-10-24 02:12
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