Update pca.py references.
@@ -248,23 +248,26 @@ class PCA(_BasePCA):
References
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- For n_components == 'mle', this class uses the method of `Thomas P. Minka:
- Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604`
+ For n_components == 'mle', this class uses the method of `Minka, T. P.
+ "Automatic choice of dimensionality for PCA". In NIPS, pp. 598-604`
Implements the probabilistic PCA model from:
- M. Tipping and C. Bishop, Probabilistic Principal Component Analysis,
- Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611-622
+ `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.
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:
- `Finding structure with randomness: Stochastic algorithms
- for constructing approximate matrix decompositions Halko, et al., 2009
- (arXiv:909)`
- `A randomized algorithm for the decomposition of matrices
- Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert`
+ `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).
+ "A randomized algorithm for the decomposition of matrices".
+ Applied and Computational Harmonic Analysis, 30(1), 47-68.`
Examples
