How can I import a Cell - Array filled with tables of data to the Classification Learner App?

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Hello,
I want to use the Classification Learner App to train a model, that can cluster a pressure map from a hand into the different parts of the hand. Like in this picture.
For training data, I already have 400 frames like the one above clustered and for each a table with X and Y coordinate and one column with the cluster, the point belongs to.
I need the model to look at every frame seperate, and not for example at all points from Cluster "5" from all the frames. I believe if I put all of the data in one table, it will cluster all of them at once, instead of frame per frame.
How can I do that? Is it even possible with the app?

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Kartik
Kartik il 17 Mag 2023
Hi,
Yes, you can train a model using the Classification Learner app in MATLAB to cluster your pressure map data into different parts of the hand. To ensure that the model looks at each frame separately, you can create a separate table for each frame and train a separate model for each table.
  5 Commenti
Kartik
Kartik il 17 Mag 2023
The clustering pattern from all the individual frames would be different, for eg.- for the same x and y, two frames can have different clusters. So if we train the model with two same datapoints having different clusters, the learnings of the model might be unpredictable or would favour that cluster more for which there are more datapoints for that particular x and y. Regarding your query for observing each frame seperately, as per my understanding, if we combine the table in such a manner that all the datapoints from every frame are sequentially together, the model would learn from all the points of one frame together.
That means copying the elements of all frames sequentially into the new single table , which would lead to it analyzing frames one after other.
Let me know if it solves your issue or if my understanding of the question is incorrect.
Ruben
Ruben il 23 Mag 2023
Yes I now understand how Classifiaction Learner App operates and what I need to do. Thanks for the good support!!

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