PCA of 6 axis

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hayder al-omairi
hayder al-omairi on 10 Nov 2022
Commented: hayder al-omairi on 26 Jan 2023
Hallo, I have 3-axis accelerometer and 3-axis gyroscope, I am planning to reduce these 6 axis to only one or two significant axis that gives more details than others by using PCA
William Rose
William Rose on 10 Nov 2022
That sounds interesting. If you want some assistance, please post some sample data and an initial attempt at code (even if it does not run) to do the principal component analysis. Good luck!
hayder al-omairi
hayder al-omairi on 26 Jan 2023
thank you it's working

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Accepted Answer

Amey Waghmare
Amey Waghmare on 21 Nov 2022
As per my understanding, you want to use Principle Component Analysis (PCA) to reduce the dimensionality of your dataset from 6 to 1 or 2 dimensions.
To perform PCA, you can use MATLAB command ‘pca’, which calculates the principal component coefficients for the dataset. You can also specify the number of components to return by using argument ‘NumComponents’.
Assume that the data is stored in variable ‘X’.
[coeff, score] = pca(X, 'NumComponents', 2);
‘coeffs’ are the principal component coefficients, and ‘score’ is the dataset in reduced dimension as specified by the ‘NumComponents’ argument.
For more information on 'pca' command, you can visit the documentation page: https://in.mathworks.com/help/stats/pca.html
Hope this resolves the issue.

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