8000 DOC: Correct links and typos in 6.6 Random Projections by star1327p · Pull Request #30848 · scikit-learn/scikit-learn · GitHub
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DOC: Correct links and typos in 6.6 Random Projections #30848

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DOC: Correct links and typos in 6.6 Random Projections #30848

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10 changes: 5 additions & 5 deletions doc/modules/random_projection.rst
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Expand Up @@ -28,7 +28,7 @@ technique for distance based method.
Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.

* Ella Bingham and Heikki Mannila. 2001.
`Random projection in dimensionality reduction: applications to image and text data. <https://citeseerx.ist.psu.edu/doc_view/pid/aed77346f737b0ed5890b61ad02e5eb4ab2f3dc6>`_
`Random projection in dimensionality reduction: applications to image and text data. <https://cs-people.bu.edu/evimaria/cs565/kdd-rp.pdf>`_
In Proceedings of the seventh ACM SIGKDD international conference on
Knowledge discovery and data mining (KDD '01). ACM, New York, NY, USA,
245-250.
Expand Down Expand Up @@ -84,7 +84,7 @@ bounded distortion introduced by the random projection::

* Sanjoy Dasgupta and Anupam Gupta, 1999.
`An elementary proof of the Johnson-Lindenstrauss Lemma.
<https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9>`_
<https://cseweb.ucsd.edu/~dasgupta/papers/jl.pdf>`_

.. _gaussian_random_matrix:

Expand All @@ -95,7 +95,7 @@ dimensionality by projecting the original input space on a randomly generated
matrix where components are drawn from the following distribution
:math:`N(0, \frac{1}{n_{components}})`.

Here a small excerpt which illustrates how to use the Gaussian random
Here is a small excerpt which illustrates how to use the Gaussian random
projection transformer::

>>> import numpy as np
Expand Down Expand Up @@ -136,7 +136,7 @@ where :math:`n_{\text{components}}` is the size of the projected subspace.
By default the density of non zero elements is set to the minimum density as
recommended by Ping Li et al.: :math:`1 / \sqrt{n_{\text{features}}}`.

Here a small excerpt which illustrates how to use the sparse random
Here is a small excerpt which illustrates how to use the sparse random
projection transformer::

>>> import numpy as np
Expand Down Expand Up @@ -179,7 +179,7 @@ been computed during fit, they are reused at each call to ``inverse_transform``.
Otherwise they are recomputed each time, which can be costly. The result is always
dense, e 577F ven if ``X`` is sparse.

Here a small code example which illustrates how to use the inverse transform
Here is a small code example which illustrates how to use the inverse transform
feature::

>>> import numpy as np
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