I have a data set that includes image arrays, point clouds, audio waveforms, and plain numerical data. I want to use unsupervised learning to group the data based on relatedness. So, if the audio and video are changing simultaneously, then the algorithm should group them together. If I am not mistaken, this is called heterogenous data clustering.
My data looks like this:
Audio [[first frame's audio samples] [second set] [third set] ... Video 4D array of shape (1654, 500, 128, 3) # of imgs l w channels Gyro [[roll, pitch, yaw], [roll, pitch, yaw], [roll, pitch, yaw], [roll, pitch, yaw]..... And a bunch of 1D numerical data
Is there a way to do this?
I am new to this so if there is some critical information missing, let me know.