[
https://issues.apache.org/jira/browse/FLINK-1733?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15133447#comment-15133447
]
Thang Nguyen commented on FLINK-1733:
-------------------------------------
Thanks [~till.rohrmann], I've been flipping through the Odersky book and it is
indeed an excellent resource.
I have some questions that may be obvious, but their answer seems to elude me
for whatever reason...
For context, I have read the sPCA paper a few times and have the Spark
implementation of sPCA running locally with a remote debugger hooked up to
validate my incremental work.
- Is it fine to use {{breeze.linalg.DenseMatrix}} for this sPCA? Matrix
multiplication with {{flink.ml.math.DenseMatrix}} doesn't seem to be
implemented as far as I can tell.
- How are DataSets partitioned across nodes, when there isn't a key explicitly
specified? Are they evenly distributed based on the size of the DataSet?
- How does parallel execution on an arbitrarily large DataSet happen from a
code perspective? Does the optimizer take care of most of the heavy lifting as
long as the code is written in a functional manner? (Asking specifically about
the FNormJob/YtXJob in the paper). I am aware of the plan visualizer, however I
haven't gotten to that point just yet...
> 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]
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
