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Diffstat (limited to 'docs/mllib-clustering.md')
| -rw-r--r-- | docs/mllib-clustering.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index f5aa15b7d9..f41ca70952 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -377,11 +377,11 @@ LDA can be thought of as a clustering algorithm as follows: on a statistical model of how text documents are generated. LDA takes in a collection of documents as vectors of word counts. -It learns clustering using [expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) -on the likelihood function. After fitting on the documents, LDA provides: +It supports different inference algorithms via `setOptimizer` function. EMLDAOptimizer learns clustering using [expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) +on the likelihood function and yields comprehensive results, while OnlineLDAOptimizer uses iterative mini-batch sampling for [online variational inference](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf) and is generally memory friendly. After fitting on the documents, LDA provides: * Topics: Inferred topics, each of which is a probability distribution over terms (words). -* Topic distributions for documents: For each document in the training set, LDA gives a probability distribution over topics. +* Topic distributions for documents: For each document in the training set, LDA gives a probability distribution over topics. (EM only) LDA takes the following parameters: |