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https://issues.apache.org/jira/browse/FLINK-1750?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15828303#comment-15828303
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Kate Eri commented on FLINK-1750:
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Hello, guys.
I have started to work on the current topic and first of all I would like to
get some context about this feature:
1) Which use cases planned to be covered by this functionality? Is this
something like monitoring case: we have many metrics to monitor and we need to
find which metrics have something critical (anomalous) and have this timeseries
something in common? Are we intended to group such time series? Or may be we
need a tool to separate dependent from independent variables?
I’m asking because all of this influence on tools to implement, because this
tasks could be solved in different ways.
2) Is there was any customer who requested this feature? Could you please
describe some reasons for this feature?
> Add canonical correlation analysis (CCA) to machine learning library
> --------------------------------------------------------------------
>
> Key: FLINK-1750
> URL: https://issues.apache.org/jira/browse/FLINK-1750
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Kate Eri
> Labels: ML
>
> Canonical correlation analysis (CCA) [1] can be used to find correlated
> features between two random variables. Moreover, CCA can be used for
> dimensionality reduction.
> Maybe the work of Jia Chen and Ioannis D. Schizas [2] can be adapted to
> realize a distributed CCA with Flink.
> Resources:
> [1] [http://en.wikipedia.org/wiki/Canonical_correlation]
> [2] [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6810359]
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