Hi Jack.
All sklearn estimators work on numpy arrays or sparse matrices.
I guess the easiest way would be to just use gensim for the feature
extraction and then feed the resulting features into sklearn.
Hth,
Andy
On 01/03/2013 10:02 PM, Jack Alan wrote:
Hi all,
I'm working in document classification and I wonder if there is a way
of having the feature vector calculated based on Latent Semantic
Indexing (LSI) instead of tf or tf-idf. As you know with LSI or Latent
Dirichlet Allocation (LDA), semantic features are captured.
I found an online Python library to do so called gensim. The point is,
how to merge gensim with sklearn to fullfill the requirement? or any
alternatives?
Jack
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