Sift - Principal Component Analysis: Difference between revisions
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[[File:SIFT_PCA_Example.png|600px|right]] | [[File:SIFT_PCA_Example.png|600px|right]] | ||
Sift provides a number of ways to visualize and interact with the results of PCA. An overview of all PCA visualizations can be found in [[Sift - Analyse Page#Principal_Component_Analysis| Sift - Analyse Page]]. | Sift provides a number of ways to visualize and interact with the results of PCA. An overview of all PCA visualizations can be found in [[Sift - Analyse Page#Principal_Component_Analysis| Sift - Analyse Page]]. | ||
More detail on the mathematics behind PCA can be found on our page: [[The_Math_of_Principal_Component_Analysis_(PCA)|The Math of Principal Component Analysis (PCA)]]. | |||
This page includes: | This page includes: | ||
* Visualizing the [[Sift - Analyse Page#Variance_Explained|variance explained by each PC individually]]; | * Visualizing the [[Sift - Analyse Page#Variance_Explained|variance explained by each PC individually]]; | ||
* Visualizing the [[Sift - Analyse Page#Loading_Vector|variance explained by each PC at each point in the signal's cycle]]; | * Visualizing the [[Sift - Analyse Page#Loading_Vector|variance explained by each PC at each point in the signal's cycle]]; | ||
* [[Sift - Analyse Page# | * [[Sift - Analyse Page#Workspace_Scores|Scatter-plotting workspace scores]] in PC-space; | ||
* Showing the [[Sift - Analyse Page#Group Scores|distribution of scores by group]] for each PC; | * Showing the [[Sift - Analyse Page#Group Scores|distribution of scores by group]] for each PC; | ||
* Visualizing the [[Sift - Analyse Page#Extreme_Plot|mean and extreme values]] that result from reconstructing the underlying data with each PC; and | * Visualizing the [[Sift - Analyse Page#Extreme_Plot|mean and extreme values]] that result from reconstructing the underlying data with each PC; and | ||
Latest revision as of 19:09, 1 May 2024
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Sift provides a number of ways to visualize and interact with the results of PCA. An overview of all PCA visualizations can be found in Sift - Analyse Page. More detail on the mathematics behind PCA can be found on our page: The Math of Principal Component Analysis (PCA).
This page includes:
- Visualizing the variance explained by each PC individually;
- Visualizing the variance explained by each PC at each point in the signal's cycle;
- Scatter-plotting workspace scores in PC-space;
- Showing the distribution of scores by group for each PC;
- Visualizing the mean and extreme values that result from reconstructing the underlying data with each PC; and
- Visualizing how the signals can be reconstructed from the computed PCs.
Further Analysis
Sift has several built in modules to take you further with you PCA Analysis, including:
- T-Squared Tests: Finding outliers using a Multi Variate T-Squared Distribution.
- Q-Tests: Finding Outliers on small datasets using a Q-Test.
- Mahalanobis Distances: Finding outliers through their Mahalanobis Distances.
- Local Outlier Factors: Finding outliers through the Local Outlier Factor.
- K-Means Analysis: Clustering PCA results through K-Means clustering.
Tutorials
For a step-by-step example of how to use Sift to perform PCA on your data, see the PCA Tutorial.
For a step-by-step example of how to use Sift to perform further statistical testing on PCA results, see the Tutorial: Run K-Means.
For a step-by-step example of processing and analyzing large data sets in Sift and using PCA to distinguish between groups, see the Tutorial: Treadmill Walking In Healthy Individuals and the Tutorial: Analysis of Baseball Hitters.