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From "Jake Mannix (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-683) LDA Vectorization
Date Thu, 28 Apr 2011 14:15:03 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-683?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13026316#comment-13026316
] 

Jake Mannix commented on MAHOUT-683:
------------------------------------

How does this compare to what is in the latest patch in MAHOUT-458 ?

> LDA Vectorization
> -----------------
>
>                 Key: MAHOUT-683
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-683
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Clustering
>            Reporter: Vasil Vasilev
>            Priority: Minor
>              Labels: LDA., Vectorization
>         Attachments: MAHOUT-683.patch
>
>
> Currently the result of LDA clustering algorithm is a state which describes the probability
of words, part of a corpus of documents, to belong to given topics. This probability is calculated
for the whole corpus
> It is interesting, however, what is the average number of words of a given document that
comes from a given topic. This information comes from the gamma vector in the LDA inference
process. This vector can be used as representation of the given document for further clustering
purposes (using algorithms like KMeans, Dirichlet, etc.). In this manner the dimensions of
a document get reduced to the number of topics that is specified to the LDA clustering algorithm.
> With the proposed implementation from a corpus of documents described as vectors and
from the last state of LDA inference process a set of vectors with reduced dimensions is produced
(a vector per a document) which represent the set of documents

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