Multiway clustering of 3-order tensor via affinity matrix

TL;DR

This paper introduces MCAM, a novel multiway clustering method for 3-order tensors that constructs an affinity matrix based on slice similarities and applies advanced clustering techniques, showing competitive results on synthetic and real data.

Contribution

The paper presents a new tensor clustering approach that leverages affinity matrices derived from slice similarities, enhancing multiway clustering performance.

Findings

MCAM achieves competitive results on synthetic datasets.

MCAM performs well on real datasets.

The method effectively captures slice similarities for clustering.

Abstract

We propose a new method of multiway clustering for 3-order tensors via affinity matrix (MCAM). Based on a notion of similarity between the tensor slices and the spread of information of each slice, our model builds an affinity/similarity matrix on which we apply advanced clustering methods. The combination of all clusters of the three modes delivers the desired multiway clustering. Finally, MCAM achieves competitive results compared with other known algorithms on synthetics and real datasets.