Nonnegative Tensor Completion: step-sizes for an accelerated variation of the stochastic gradient descent
European Signal Processing Conference (EUSIPCO)(2022)
摘要
We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix least-squares problem via an accelerated variation of the stochastic gradient descent. The step-sizes used by the algorithm determine, to a high extent, its behavior. We propose two new strategies for the computation of step-sizes and we experimentally test their effectiveness using both synthetic and real-world data.
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关键词
accelerated variation,step-sizes
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