Noise-free latent block model for high dimensional data


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.

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    The datasets can be found here: and the code will be available upon publication.

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Correspondence to Charlotte Laclau.

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Laclau, C., Brault, V. Noise-free latent block model for high dimensional data. Data Min Knowl Disc 33, 446–473 (2019).

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  • Latent block model
  • Feature selection
  • Clustering
  • Biclustering
  • High dimensional data