[MADLIB-990] SVM - novelty detection using 1-class SVM

• Type: New Feature
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: v1.9.1
• Component/s: Module: Support Vector Machines
• Assignee: Nandish Jayaram
• Reporter: Frank McQuillan
• Created: 13/Apr/16 20:41
• Updated: 17/Aug/16 22:22
• Resolved: 29/Jun/16 00:48

Description

Story

As a data scientist, I want to use a one-class SVM so that I can decide whether a new observation belongs to the same distribution as existing observations (an inlier), or should be considered as different (an outlier).

Acceptance

1) One-class SVM implemented with all supported kernel types (linear, gaussian, polynomial).
2) Output a T/F for not-novel/novel.

Note

a) Similar e1071 R package [1] with
type=one-classification (for novelty detection)

b) There is an important distinction between novelty detection (this story) and outlier detection for cleaning training data. From reference [2]:

• novelty detection: the training data is not polluted by outliers, and we are interested in detecting anomalies in new observations. <- this story
• outlier detection: the training data contains outliers, and we need to fit the central mode of the training data, ignoring the deviant observations. <- we are not trying to solve this unsupervised learning problem in this story.

References

[1] e1071 R package
https://cran.r-project.org/web/packages/e1071/index.html

[2] Difference between novelty and outlier detection
http://scikit-learn.org/stable/modules/outlier_detection.html

Issue Links

• links to: GitHub Pull Request #48