Noise-free Latent Block Model for High Dimensional Data

Charlotte Laclau (1) , Vincent Brault (2)

1 LHC - Laboratoire Hubert Curien
2 SVH - Statistique pour le Vivant et l’Homme

Co-clustering is known to be a very powerful and efficient approach in unsupervised learning because of its ability to partition data based on both the observations and the variables of a given dataset. However, in high-dimensional context co-clustering methods may fail to provide a meaningful result due to the presence of noisy and/or irrelevant features. In this paper, we tackle this issue by proposing a novel co-clustering model which assumes the existence of a noise cluster, that contains all irrelevant features. A variational expectation-maximization (VEM)-based algorithm is derived for this task, where the automatic variable selection as well as the joint clustering of objects and variables are achieved via a Bayesian framework. Experimental results on synthetic datasets show the efficiency of our model in the context of high-dimensional noisy data. Finally, we highlight the interest of the approach on two real datasets which goal is to study genetic diversity across the world.

Domaines: Théorie [stat.TH]; Machine Learning [stat.ML]; Méthodologie [stat.ME]; Applications [stat.AP]

Charlotte Laclau, Vincent Brault. Noise-free Latent Block Model for High Dimensional Data. Data Mining and Knowledge Discovery, 2019, 33 (2), pp.446-473. ⟨10.1007/s10618-018-0597-3⟩. ⟨hal-01685777v2⟩