Noise-free latent block model for high dimensional data

• Laclau, Charlotte ^[1] ; Brault, Vincent ^[1]
  1. [1] Slovak University of Technology in Bratislava

     Slovak University of Technology in Bratislava

     Eslovaquia

     
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 33, Nº 2, 2019, págs. 446-473
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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-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.