Sift Tutorial: Outlier Detection with PCA: Difference between revisions
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==Data== | ==Data== | ||
For this tutorial, we will be examining some [[https://www.c-motion.com/download/examples/V3D_Workshop.zip|gait data from a Visual3D Workshop]] at a recent ASB meeting. | |||
(HipAngleX - Traces: 2 distinct groups and several outliers) | (HipAngleX - Traces: 2 distinct groups and several outliers) | ||
(also HipAngleZ from auto builder) | (also HipAngleZ from auto builder) | ||
Revision as of 13:34, 1 May 2024
| Language: | English • français • italiano • português • español |
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This tutorial will show you how to use the outlier detection methods built into Sift, when each method might be appropriate and how to interpret the results. Outlier detection is a key artifact of data analysis, identifying errant data or anomalies that might make further data analysis less effective. Within Sift, there are multiple methods for detecting outliers, but we will be focusing on doing so with PCA data in this tutorial.
Data
For this tutorial, we will be examining some [data from a Visual3D Workshop] at a recent ASB meeting.
(HipAngleX - Traces: 2 distinct groups and several outliers) (also HipAngleZ from auto builder)