What does the long curve-shape t-SNE mean? I use 1-D CNN input 1*512 size time series data which randomly fragment segment, the output will classify input into 10 classes. After training the CNN, I apply t-SNE to the prediction which I fed in testing data. 
In general, the output shape of the tsne result is spherical(for example,applied on MNIST dataset). But now I apply t-SNE on my own dataset. No matter how I adjust perplexity early, learning rate or maximum number of iterations. It will give me the result of long-shaped output, just like the pic below. Does the long-shaped t-SNE mean anything? Thanks everyone beforehand.



Edit: Explaining about dataset contents. p.s. before feeding into CNN for training, I randomly split it into training/testing dataset.
The lines therefore are the time series. t-sne is a very locally sensitive algorithm so every data point is very likely to show up nearest (in 2-D) to its nearest (N-D) neighbour. 

In MNIST there is no sequence, so it doesn't show up as a line in 2-D. Every digit is written in a set of similar ways that cluster together. In your time-series data, if each time point is most similar to its next time point, what we expect is for them to be in some sequence - because you'd want point t=n to be closest to points t=n-1 and t=n+1  (if they are in fact the most similar).