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I am working on building a model to classify the type of touch the user makes (Long Press, Left Swipe, Right swipe, and so on). I have data with features that characterize the user's touch, like duration, velocity in x-direction, velocity in y-direction, etc. One feature that is also present is the trajectory of the touch.

The problem is that for touches like taps or long-press, the length of the trajectory array is 2 or 3 points, but for swipes, it reaches up to 40-100 points. What I thought could work is either using padding or CNNs. But the problem with padding is that as I am using trajectories, if I pad them with 0s it might affect the learning because a '0' still has meaning in trajectories as some points. And what I think the problem might be with CNNs is that first I do not know if such an architecture could work for all features (touch duration, xVelocity, etc.) as they are not spatially related. I may be wrong about this; feel free to correct it. I also thought of using RNNs, but I did not as they are mainly used for NLP tasks and all the features are not related to each other sequentially.

What are the different ways I can handle this kind of variable-sized input feature for neural networks?

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Please check this link https://sequentia.readthedocs.io/en/latest/sections/classifiers/knn.html Dynamic Time Warping k-Nearest Neighbors Classifier

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