Online implementation of Non-negative Matrix Factorizarion (NMF)
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#### Description
<!-- Example: Joblib Error thrown when calling fit on LatentDirichletAllocation with evaluate_every > 0-->
@GaelVaroquaux Maybe an online version of the NMF could be useful in big data settings.

#### Steps/Code to Reproduce
<!--
Example:
```python
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

docs = ["Help I have a bug" for i in range(1000)]

vectorizer = CountVectorizer(input=docs, analyzer='word')
lda_features = vectorizer.fit_transform(docs)

lda_model = LatentDirichletAllocation(
    n_topics=10,
    learning_method='online',
    evaluate_every=10,
    n_jobs=4,
)
model = lda_model.fit(lda_features)
```
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#### Expected Results
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#### Actual Results
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Here I show the results for an sparse matrix as input of size 1M x 2.8k
The factorization has `n_components = 10` and each point is an entire pass on the data.
The current online implementation only supports the kullback-leibler divergence, but in practice
can be generalized to any beta divergence.

![batch_vs_online_nmf](https://user-images.githubusercontent.com/11759948/53494592-69748b00-3a9e-11e9-8cc8-ce53ce3fe7f2.png)

#### Versions
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Draft implementation of MiniBatchNMF
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#### Reference Issues/PRs
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#### What does this implement/fix? Explain your changes.

Implementation of a mini-batch NMF with multiplicative updates.
#13308 
#### Any other comments?


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