Unifying references style in docstrings in _pca.py

### Describe the issue linked to the documentation

A very minor suggested change to write references section of function docstrings in identical style in [_pca.py](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/decomposition/_pca.py) code file.

### Suggest a potential alternative/fix

I aimed to write both references in a single identical style to improve documentation style. I followed the [issue creation link](https://github.com/scikit-learn/scikit-learn/issues/new/choose), where I chose [‘Documentation improvement’ option](https://github.com/scikit-learn/scikit-learn/issues/new?assignees=&labels=Documentation%2CNeeds+Triage&projects=&template=doc_improvement.yml), which provided a template to submit an appropriate issue. Several things that have changed are:
•	Followed the [Python PEP8 style guide](https://peps.python.org/pep-0008/#maximum-line-length), as the ‘[Coding guidelines](https://scikit-learn.org/dev/developers/develop.html#coding-guidelines)’ from the project specified.
•	Followed `author (year). “title”. journal name and page. <link>` format.
•	Changed link addresses from https to doi whenever possible.
•	Changed two different link to identical literature between the two references to identical link.

ORIGINAL (line 51 to 54)

This implements the method of `Thomas P. Minka:
Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604
<https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_

POST-EDIT

This implements the method from:
`Minka, T. P.. (2000). “Automatic choice of dimensionality for PCA”.
NIPS 2000, 598-604.
<https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_


ORIGINAL (line 324 to 347)

For n_components == 'mle', this class uses the method from:
`Minka, T. P.. "Automatic choice of dimensionality for PCA".
In NIPS, pp. 598-604 <https://tminka.github.io/papers/pca/minka-pca.pdf>`_

Implements the probabilistic PCA model from:
`Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal
component analysis". Journal of the Royal Statistical Society:
Series B (Statistical Methodology), 61(3), 611-622.
<http://www.miketipping.com/papers/met-mppca.pdf>`_
via the score and score_samples methods.

For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.

For svd_solver == 'randomized', see:
:doi:`Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).
"Finding structure with randomness: Probabilistic algorithms for
constructing approximate matrix decompositions".
SIAM review, 53(2), 217-288.
<10.1137/090771806>`
and also
:doi:`Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).
"A randomized algorithm for the decomposition of matrices".
Applied and Computational Harmonic Analysis, 30(1), 47-68.
<10.1016/j.acha.2010.02.003>`

POST-EDIT

For n_components == 'mle', this class uses the method from:
`Minka, T. P.. (2000). “Automatic choice of dimensionality for PCA”.
NIPS 2000, 598-604. 
<https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_

Implements the probabilistic PCA model from:
:doi:`Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal
component analysis". Journal of the Royal Statistical Society:
Series B (Statistical Methodology), 61(3), 611-622.
<10.1162/089976699300016728>`
via the score and score_samples methods.

For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.

For svd_solver == 'randomized', see:
:doi:`Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).
"Finding structure with randomness: Probabilistic algorithms for
constructing approximate matrix decompositions".
SIAM review, 53(2), 217-288.
<10.1137/090771806>`
and also
:doi:`Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).
"A randomized algorithm for the decomposition of matrices".
Applied and Computational Harmonic Analysis, 30(1), 47-68.
<10.1016/j.acha.2010.02.003>`

COMMENT:
I agree that the changes are very minor, frankly speaking I am a student in Open Source class trying to learn how Open Source communities develop. Appreciate your feedback, lesteve!

Labels: Documentation