We are delighted to announce the next talk in our NeST Online Seminar Series.
Thursday 17 December 2026, 1pm (London time)
Title: Consistent spectral clustering in sparse tensor block models
Speaker: Prof. Lasse Leskelä (Aalto University)
Abstract:
High-order clustering aims to classify objects in multiway datasets that are prevalent in various fields such as bioinformatics, social network analysis, and recommendation systems. These tasks often involve data that is sparse and high-dimensional, presenting significant statistical and computational challenges. This paper introduces a tensor block model specifically designed for sparse integer-valued data tensors. We propose a simple spectral clustering algorithm augmented with a trimming step to mitigate noise fluctuations, and identify a density threshold that ensures the algorithm’s consistency. Our approach models sparsity using a sub-Poisson noise concentration framework, accommodating heavier than sub-Gaussian tails. Remarkably, this natural class of tensor block models is closed under aggregation across arbitrary modes. Consequently, we obtain a comprehensive framework for evaluating the tradeoff between signal loss and noise reduction during data aggregation. The analysis is based on a novel concentration bound for sparse random Gram matrices. The theoretical findings are illustrated through simulation experiments.
Based on joint work with Ian Välimaa (https://arxiv.org/abs/2501.13820).
For further details, please contact one of the online seminar organisers, Jiaming Chen (j.chen1@imperial.ac.uk) or Amandine Pierrot (amcp23@bath.ac.uk) for further information and/or the link to the seminar.
