Body: <p>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 characterise the user's touch, like duration, velocity in x-direction, velocity in y-direction etc. One feature that's also present is the <strong>trajectory</strong> of the touch.</p>
  <p>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 can work is either use padding or use 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 don't know if such  an architecture could work for <strong>all features</strong> (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.</p>
  <p>What are the different ways I can handle this kind of variable-sized input feature for neural networks?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-10T16:04:24.977Z
Comments.Text: "all the features are not related to each other sequentially." - what? Trajectory points **are** related to each other sequentially!
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-10T17:35:29.433Z
Comments.Text: yeah but as I mentioned, they are just one feature of the model, others include touchDuration, x-velocity, y-velocity etc and so they are not sequentially related to the trajectory right? That's why I made the 'all features' bold
Comments.UserDisplayName: Ravish Jha
Tags: <neural-networks><machine-learning><deep-learning><convolutional-neural-networks><feature-engineering>
Title: How to pass variable length data as feature to a neural network?
UserDisplayName: Ravish Jha
ThreadText: # How to pass variable length data as feature to a neural network?
  
  (Asked by: username_0 on 2021-08-10)
  
  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 characterise the user's touch, like duration, velocity in x-direction, velocity in y-direction etc. One feature that's 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 can work is either use padding or use 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 don't 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?
  
  **Tags:** neural-networks, machine-learning, deep-learning, convolutional-neural-networks, feature-engineering
  > **Comments:**
  > **username_1**: "all the features are not related to each other sequentially." - what? Trajectory points **are** related to each other sequentially!  
  > **username_0**: yeah but as I mentioned, they are just one feature of the model, others include touchDuration, x-velocity, y-velocity etc and so they are not sequentially related to the trajectory right? That's why I made the 'all features' bold  
  
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  ## 0 answers
  
  