Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[ENH] DOBIN transformer implementation #3371

Closed
KatieBuc opened this issue Sep 1, 2022 · 1 comment · Fixed by #3373
Closed

[ENH] DOBIN transformer implementation #3371

KatieBuc opened this issue Sep 1, 2022 · 1 comment · Fixed by #3373
Assignees
Labels
enhancement Adding new functionality implementing algorithms Implementing algorithms, estimators, objects native to sktime module:annotation

Comments

@KatieBuc
Copy link
Contributor

KatieBuc commented Sep 1, 2022

Distance based Outlier BasIs using Neighbors (DOBIN).

DOBIN is a pre-processing algorithm that constructs a set of basis
vectors tailored for outlier detection as described by _[1]. DOBIN
has a simple mathematical foundation and can be used as a dimension
reduction tool for outlier detection tasks.

Currently (only) implemented in R.

Reference:
[1] Kandanaarachchi, Sevvandi, and Rob J. Hyndman. "Dimension reduction
for outlier detection using DOBIN." Journal of Computational and Graphical
Statistics 30.1 (2021): 204-219.

Paper:
https://robjhyndman.com/papers/dobin.pdf

Code to re-write:
https://github.com/sevvandi/dobin/blob/master/R/dobin.R

@KatieBuc KatieBuc added the enhancement Adding new functionality label Sep 1, 2022
@lmmentel lmmentel added implementing algorithms Implementing algorithms, estimators, objects native to sktime module:annotation labels Sep 1, 2022
fkiraly pushed a commit that referenced this issue Sep 25, 2022
Implementation of Issue #3371

Distance based Outlier BasIs using Neighbors (DOBIN) is a new approach to select a set of basis vectors tailored for outlier detection. The transformation outputs are the new coordinate system (such that outliers are best retained in lower the dimensional feature space).
@lmmentel lmmentel linked a pull request Oct 3, 2022 that will close this issue
@lmmentel
Copy link
Contributor

lmmentel commented Oct 3, 2022

closed by #3373

@lmmentel lmmentel closed this as completed Oct 3, 2022
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
enhancement Adding new functionality implementing algorithms Implementing algorithms, estimators, objects native to sktime module:annotation
Development

Successfully merging a pull request may close this issue.

2 participants