can we implement PCA for lets 10 subjects 3 repeated trials with each trial 101 samples and variable vertical-GRF i.e. 101*30 input matrix
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Principal Component Analysis
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Kartik Mittal
le 25 Sep 2018
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My understanding of the problem is that you have 101 data points, each with 30 attributes? If that is the case, you can use PCA. But I am not sure you have a correct feature vector for what you are calling the data points (samples). If you have 10 subjects, with 3 trials, how are they attributive of one sample? In your case PCA would make sense if there is some correlation between trials, like stages of an experiment or something.
4 commentaires
imran mahmood
le 25 Sep 2018
Kartik Mittal
le 27 Sep 2018
- It would make more sense if you could describe what your predictors are. As I stated in my answer above, "I am not sure you have a correct feature vector for what you are calling the data points (samples)." The interpretation : 3 trials x 10 subjects = 30 features, might not be correct when you are trying to use PCA. I don't understand fully what your workflow is.
- You can use PCA if you are clear about 1.
imran mahmood
le 27 Sep 2018
Kartik Mittal
le 28 Sep 2018
Thanks for the information, it makes the case clear that you will have repeated measures as your data points (given all the trials are with one experimental condition). Hence, PCA would not be an obvious choice for what you wish to do. Check this link - https://stats.stackexchange.com/questions/18617/can-i-do-a-pca-on-repeated-measures-for-data-reduction
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