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https://issues.apache.org/jira/browse/FLINK-1733?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15113112#comment-15113112
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Thang Nguyen commented on FLINK-1733:
-------------------------------------
Hi [~till.rohrmann]!
I am a software engineer professionally, however I am new to Scala. I did learn
some functional programming in undergrad, so the trickiest thing for me to wrap
my head around is Scala's type system.
For context:
I have a naive PCA implementation and some trivial tests for it (using the
method & test data from this paper:
http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf).
Currently, the method accepts an Int (# of princ. components), and a
DataSet[DenseVector].
For this implementation, I create a covariance matrix (BreezeMatrix) and call
breeze.linalg.eigSym on that.
Then I return the top N (user param) principal components as a DataSet[Vector].
I will be re-factoring/throwing out a lot of my code (except the tests), so I
hesitate to show anything I've written just yet.
Questions:
Does the method signature make sense?
What _exactly_ should I be returning? The concept of PCA is new to me but it
sounds like I should be returning the top N vectors (based on their
eigenvalues, ordered by significance).
Should the output also be DataSet[DenseVector]?
Pointers on how to implement sPCA?
I have taken a cursory look at the rest of the ML library but I am still
learning Scala.
If you have any recommended resources on learning Scala (specifically the type
system), I would also appreciate that.
Thanks!
Thang
> 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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