  Popular crowdsourcing techniques mostly focus on evaluating workers' labeling
quality before adjusting their weights during label aggregation. Recently,
another cohort of models regard crowdsourced annotations as incomplete tensors
and recover unfilled labels by tensor completion. However, mixed strategies of
the two methodologies have never been comprehensively investigated, leaving
them as rather independent approaches. In this work, we propose $\textit{MiSC}$
($\textbf{Mi}$xed $\textbf{S}$trategies $\textbf{C}$rowdsourcing), a versatile
framework integrating arbitrary conventional crowdsourcing and tensor
completion techniques. In particular, we propose a novel iterative Tucker label
aggregation algorithm that outperforms state-of-the-art methods in extensive
experiments.
