Parallel Computation of Multi-Slice Clustering of Third-Order Tensors
This work addresses computational efficiency for researchers handling large tensor data, but it is incremental as it focuses on parallelizing an existing method.
The paper tackled the challenge of clustering massive third-order tensor datasets by developing parallel algorithms for Multi-Slice Clustering, which outperformed sequential computing and enabled scalability.
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.