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I have a binary classification problem, where each data point is multi-channel time-series, which can be represented as a matrix $T \times F$, where $T$ is the time-series length, and $F$ as the channels number.
$T$ isn't constant - each data point has different length. For classification I need to extract a fixed sized features vector from each data point. Moreover, I suspect that the information is partly embedded as short events in the data, so transformations as fourier are not very helpful.
I tried to calculate the covariance matrix of $T\times F$ to get a $F\times F$ matrix, take the upper/lower part (include the main diagonal) and flat it. After that, I used PCA to reduce dimensionality.

I'm afraid I loosing lots of important information using this transformation. Mainly because SVM gives me something near to chance level. I don't think that methods like Matrix Completion will help, maybe stretching methods will be more helpful.

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closed as unclear what you're asking by whuber Apr 21 at 22:12

Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question.If this question can be reworded to fit the rules in the help center, please edit the question.

    
Is there any meaning to this matrix representation? Its structure implies, for instance, that all the channel values for a given row of the matrix are somehow related. Your subsequent description of the data, however, suggests not: it indicates you really just have some kind of list of time series and if there is any relationship among them it occurs at common values of time (which somehow must be attached, explicitly or implicitly, to each column). It will also be difficult to attack this problem until you provide some details about how a "short" "event" might be identified. –  whuber Apr 21 at 22:12