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.
