DOC Add URL to reference of Minka paper used in PCA
@@ -32,7 +32,8 @@ def _assess_dimension(spectrum, rank, n_samples):
"""Compute the log-likelihood of a rank ``rank`` dataset.
The dataset is assumed to be embedded in gaussian noise of shape(n,
- dimf) having spectrum ``spectrum``.
+ dimf) having spectrum ``spectrum``. This implements the method of
+ T. P. Minka.
Parameters
----------
@@ -50,10 +51,11 @@ def _assess_dimension(spectrum, rank, n_samples):
ll : float
The log-likelihood.
- Notes
- -----
+ References
+ ----------
This implements the method of `Thomas P. Minka:
- Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604`
+ Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604
+ <https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_
"""
n_features = spectrum.shape[0]
@@ -271,26 +273,30 @@ class PCA(_BasePCA):
References
----------
- For n_components == 'mle', this class uses the method of *Minka, T. P.
- "Automatic choice of dimensionality for PCA". In NIPS, pp. 598-604*
+ 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
+ `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.
- See http://www.miketipping.com/papers/met-mppca.pdf
For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.
For svd_solver == 'randomized', see:
- *Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).
+ `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.* and also
- *Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).
+ SIAM review, 53(2), 217-288.
+ <https://doi.org/10.1137/090771806>`_
+ and also
+ `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.*
+ Applied and Computational Harmonic Analysis, 30(1), 47-68
+ <https://doi.org/10.1016/j.acha.2010.02.003>`_.
Examples
--------
