28.2.2020 Fast tensorial independent component analysis - Joni Virta (University of Turku) 754ebb0e592a48c9b64ad84f7f8609ff

A novel method of tensorial independent component analysis is proposed based on TJADE and k-JADE, two recently proposed generalizations of the classical JADE algorithm. The new method achieves consistency and the limiting distribution of TJADE under mild assumptions, and at the same time offers notable improvement in computational speed. A trade-off between computational speed and assumptions is controlled by a tuning parameter which has a natural interpretation as the maximal kurtosis multiplicity. Simulations and timing comparisons demonstrate the method's gain in speed and show that the desired efficiency is obtained approximately also for finite samples. The method is applied successfully to large-scale video data, for which neither TJADE nor k-JADE is feasible. Finally, an experimental procedure is proposed to select the values of a set of tuning parameters. Joint work with Niko Lietzén, Pauliina Ilmonen, and Klaus Nordhausen.

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