Online Tensor Factorization for Feature Selection in EEG
نویسنده
چکیده
Tensor decompositions are a valuable tool in data analysis, but the computational cost of standard tensor algorithms quickly becomes prohibitive, especially when considering large and time-evolving data sets such as those found in signal processing applications. In this work multilinear PCA, a common tensor analysis technique, will be modified to enable the processing of large scale tensorial time-evolving data, such as EEG, with much improved performance both in terms of memory and CPU time.
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