Parallel Computation of Multi-Slice Clustering of Third-Order Tensors
Dina Faneva Andriantsiory Camille Coti Joseph Ben Geloun Mustapha Lebbah
Machine Learning approaches like clustering methods deal with massive
datasets that present an increasing challenge. We devise parallel algorithms to
compute the Multi-Slice Clustering (MSC) for 3rd-order tensors. The MSC method
is based on spectral analysis of the tensor slices and works independently on
each tensor mode. Such features fit well in the parallel paradigm via a
distributed memory system. We show that our parallel scheme outperforms
sequential computing and allows for the scalability of the MSC method.