DOC Correct links and typos in 6.6 Random Projections (#30848)

star1327p · web-flow · commit 6d14a5885c5f · 2025-02-18T10:12:49.000+01:00
diff --git a/doc/modules/random_projection.rst b/doc/modules/random_projection.rst
@@ -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.
@@ -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:
 
@@ -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
@@ -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
@@ -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, even 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
