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From "Apache Spark (JIRA)" <>
Subject [jira] [Commented] (SPARK-21209) Implement Incremental PCA algorithm for ML
Date Thu, 01 Mar 2018 19:49:00 GMT


Apache Spark commented on SPARK-21209:

User 'sandecho' has created a pull request for this issue:

> Implement Incremental PCA algorithm for ML
> ------------------------------------------
>                 Key: SPARK-21209
>                 URL:
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>    Affects Versions: 2.1.1
>            Reporter: Ben St. Clair
>            Priority: Major
>              Labels: features
> Incremental Principal Component Analysis is a method for calculating PCAs in an incremental
fashion, allowing one to update an existing PCA model as new evidence arrives. Furthermore,
an alpha parameter can be used to enable task-specific weighting of new and old evidence.
> This algorithm would be useful for streaming applications, where a fast and adaptive
feature subspace calculation could be applied. Furthermore, it can be applied to combine PCAs
from subcomponents of large datasets.

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