I have some unlabeled 1D (i.e. time-domain) signals (real neuron measurements) that I would like to classify in 3 classes. I would like to use a ConvNet to do this. However, as far as I know, ConvNets are trained in a supervised fashion.
I've done some research and I've found this paper that I'm not completely sure I understand. Does it use an improved version of the k-means algorithm to find the filters that best extract the features for the inputs to be clustered? I don't understand why the ConvNet would be of use here, if we are just using k-means to cluster the data. I've also found this question, which I believe is related to my problem but I'm not quite sure if it applies directly.
In a nutshell, my question is: how could I use a CNN to classify unlabeled one-dimensional data?