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Aside from Convolution Neural Networks, are there any other methods that allow for classification of Tensors? My observations consist of multi-dimensional tensors with height of 1, where each channel corresponds to a particular time-series and am wondering how I can effectively classify the tensors, taking into account the relationship between the time-series.

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  • $\begingroup$ You can flatten the tensor and run the usual machine learning methods on the vector: random forest, kNN, SVM, logistic regression, etc. $\endgroup$ – Dave Apr 20 at 1:27
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Recurrent neural networks might be an option. These have the capability of keeping information in a time series and are used for signals in time domain, for applications such as speech recognition etc... LSTM is a variant of these which is also widely used.

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    $\begingroup$ Very good point, but my issue is that I don't have one-to-one labelling, but rather one label for the entire tensor. I was under the impression that RNNs require labels at every time-step. Is there any other method that comes to your mind? $\endgroup$ – mamafoku Aug 18 '17 at 15:43
  • $\begingroup$ Can you use the same label at each time step? If the label is cat then it’s always a cat, right? $\endgroup$ – Dave Apr 20 at 1:22

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