DOC Fix broken links (scikit-learn#34491)

Author: DeaMariaLeon

doc/institutional_support.rst

@@ -163,7 +163,8 @@ funded one year for a developer to work on the project full-time (2014-2015), 50
 of the time of Guillaume Lemaitre (2016-2017) and 50% of the time of Joris van den
 Bossche (2017-2018).
 
-`NYU Moore-Sloan Data Science Environment <https://cds.nyu.edu/mooresloan/>`_
+`NYU Moore-Sloan Data Science Environment
+<https://web.archive.org/web/20240719150041/https://cds.nyu.edu/mooresloan/>`_
 funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan
 Data Science Environment also funds several students to work on the project
 part-time.
@@ -172,7 +173,7 @@ part-time.
 (2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot
 (2016-2017) and Albert Thomas (2017) to work on scikit-learn.
 
-`The Labex DigiCosme <https://digicosme.lri.fr>`_ funded Nicolas Goix
+`The Labex DigiCosme <https://anr.fr/ProjetIA-11-LABX-0045>`_ funded Nicolas Goix
 (2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias
 (2018-2019) to work part time on scikit-learn during their PhDs. It also
 funded a scikit-learn coding sprint in 2015.

doc/modules/decomposition.rst

@@ -297,7 +297,7 @@ prediction (kernel dependency estimation). :class:`KernelPCA` supports both
 
 .. [Scholkopf1997] Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller.
    `"Kernel principal component analysis."
-   <https://people.eecs.berkeley.edu/~wainwrig/stat241b/scholkopf_kernel.pdf>`_
+   <https://graphics.stanford.edu/courses/cs233-25-spring/ReferencedPapers/scholkopf_kernel.pdf>`_
    International conference on artificial neural networks.
    Springer, Berlin, Heidelberg, 1997.
 

doc/modules/ensemble.rst

@@ -493,8 +493,8 @@ Finally, many parts of the implementation of
    BoostingDecision Tree" <https://papers.nips.cc/paper/
    6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`_
 
-.. [Fisher1958] Fisher, W.D. (1958). `"On Grouping for Maximum Homogeneity"
-   <http://csiss.ncgia.ucsb.edu/SPACE/workshops/2004/SAC/files/fisher.pdf>`_
+.. [Fisher1958] Fisher, W.D. (1958). :doi:`"On Grouping for Maximum Homogeneity"
+   <10.1080/01621459.1958.10501479>`
    Journal of the American Statistical Association, 53, 789-798.
 
 
@@ -927,8 +927,8 @@ based on permutation of the features.
    boosting machine <10.1214/aos/1013203451>`.
    Annals of Statistics, 29, 1189-1232.
 
-.. [Friedman2002] Friedman, J.H. (2002). `Stochastic gradient boosting.
-   <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=48caac2f65bce47f6d27400ae4f60d8395cec2f3>`_.
+.. [Friedman2002] Friedman, J.H. (2002). :doi:`Stochastic gradient boosting.
+   <10.1016/S0167-9473(01)00065-2>`.
    Computational Statistics & Data Analysis, 38, 367-378.
 
 .. [R2007] G. Ridgeway (2006). `Generalized Boosted Models: A guide to the gbm

doc/modules/feature_selection.rst

@@ -239,9 +239,8 @@ alpha parameter, the fewer features selected.
 
   .. rubric:: References
 
-  Richard G. Baraniuk "Compressive Sensing", IEEE Signal
-  Processing Magazine [120] July 2007
-  http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf
+  Richard G. Baraniuk :doi:`"Compressive Sensing" <10.1109/MSP.2007.4286571>`,
+  IEEE Signal Processing Magazine [120] July 2007
 
 
 Tree-based feature selection
@@ -320,9 +319,9 @@ with *all* the features and greedily *remove* features from the set. The
 
   .. rubric:: References
 
-  .. [sfs] Ferri et al, `Comparative study of techniques for
+  .. [sfs] Ferri et al, :doi:`Comparative study of techniques for
       large-scale feature selection
-      <https://citeseerx.ist.psu.edu/doc_view/pid/5fedabbb3957bbb442802e012d829ee0629a01b6>`_.
+      <10.1016/B978-0-444-81892-8.50040-7>`.
 
 
 .. rubric:: Examples

examples/covariance/plot_mahalanobis_distances.py

@@ -52,8 +52,8 @@
 
 .. rubric:: References
 
-.. [1] P. J. Rousseeuw. `Least median of squares regression
-    <http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/LeastMedianOfSquares.pdf>`_. J. Am
+.. [1] P. J. Rousseeuw. :doi:`Least median of squares regression
+    <10.1080/01621459.1984.10477105>`. J. Am
     Stat Ass, 79:871, 1984.
 .. [2] Wilson, E. B., & Hilferty, M. M. (1931). `The distribution of chi-square.
     <https://water.usgs.gov/osw/bulletin17b/Wilson_Hilferty_1931.pdf>`_