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https://issues.apache.org/jira/browse/FLINK-1733?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15133521#comment-15133521
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Thang Nguyen commented on FLINK-1733:
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| I'm not sure that {{DenseMatrix}} fits for sPCA
What if the Matrix is relatively small?
>From the paper, where d is the # of principal components:
| matrix C, which is of size D × d (recall that d is typically small). For
example, in our experiments with a 94 GB dataset, the size of matrix C was 30
MB, which can easily fit in memory.
This matrix C is broadcasted to the workers and is used to redundantly
recompute an intermediate matrix (in favor of cutting down communication
complexity). The distributed algorithm also only requires accessing a single
row at a time to compute a partial result, and then sums the partials at the
end.
Is the lack of a distributed matrix/vector implementation enough of a blocker
to be worried, or should I continue?
> Add PCA to machine learning library
> -----------------------------------
>
> Key: FLINK-1733
> URL: https://issues.apache.org/jira/browse/FLINK-1733
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Thang Nguyen
> Priority: Minor
> Labels: ML
>
> Dimension reduction is a crucial prerequisite for many data analysis tasks.
> Therefore, Flink's machine learning library should contain a principal
> components analysis (PCA) implementation. Maria-Florina Balcan et al. [1]
> proposes a distributed PCA. A more recent publication [2] describes another
> scalable PCA implementation.
> Resources:
> [1] [http://arxiv.org/pdf/1408.5823v5.pdf]
> [2] [http://ds.qcri.org/images/profile/tarek_elgamal/sigmod2015.pdf]
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