[MRG] Incremental PCA

An implementation of Incremental PCA. See [Incremental Learning for Visual Tracking, J. Lim, D. Ross, R. Lin, M. Yang, 2004](http://www.cs.toronto.edu/~dross/ivt/LimRossLinYang_nips04.pdf) for more details.

The key idea behind this PR is to have an out-of-core PCA that gives roughly equivalent results while using constant memory (on the order of the ```batch_size``` rather than ```n_samples```).
Yes I had it as a placholder for pipeline (transform as well). I will add a
docstring about being a placeholder for Pipeline compatibility


On Mon, Aug 18, 2014 at 11:31 AM, Arnaud Joly <notifications@github.com>
wrote:

> In sklearn/decomposition/incremental_pca.py:
>
> > +                 batch_size=None):
> > +        self.n_components = n_components
> > +        self.whiten = whiten
> > +        self.copy = copy
> > +        self.batch_size = batch_size
> > +        self.components_ = None
> > +
> > +    def fit(self, X, y=None):
> > +        """Fit the model with X, using minibatches of size batch_size.
> > +
> > +        Parameters
> > +        ----------
> > +        X: array-like, shape (n_samples, n_features)
> > +            Training data, where n_samples is the number of samples and
> > +            n_features is the number of features.
> > +
>
> Yes, otherwise you don't understand why there is a y. Something like this
> could work
>
> y : is not used: placeholder to allow for usage in a Pipeline.
>
> —
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> <https://github.com/scikit-learn/scikit-learn/pull/3285/files#r16344045>.
>