No abstract

No abstract

International audienceSeriation is a useful statistical method to visualize clusters in a dataset. However, as the data are noisy or unbalanced, visualizing the data structure becomes challenging. To alleviate this limitation, we introduce a novel metric based on common neighborhood to evaluate the degree of sparsity in a dataset. A pile of matrices are derived for different levels of sparsity, and the matrices are permuted by a branch-and-bound algorithm. The matrix with the best block diagonal form is then selected by a compactness criterion. The selected matrix reveals the intrinsic structure of the data by excluding noisy data or outliers. This seriation algorithm is applicable even if the number of clusters is unknown or if the clusters are imbalanced. However, if the metric introduces too much sparsity in the data, the sub-sampled groups of data could be ousted. To resolve this problem, a multi-scale approach combining different levels of sparsity is proposed. The capability of the proposed seriation method is examined both by toy problems and in the context of spike sorting

No abstract

Abstract. Implementing probabilistic models in the Very-Large-ScaleIntegration (VLSI) has been attractive to implantable biomedical devices for improving sensor fusion and power management. However, implantable devices are normally exposed to noisy environments which can introduce non-negligible computational errors and hinder optimal modelling on-chip. While the probablistic model called the Continuous Restricted Boltzmann Machine (CRBM) has been shown useful and realised as a VLSI system with noise-induced stochastic behaviour, this paper investigates the suggestion that the stochastic behaviour in VLSI could enhance the tolerance against the interferences of environmental noise. The behavioural simulation of the CRBM system is used to examine the system's performance in the presence of environmental noise. Furthermore, the possibility of using environmental noise to induce stochasticity in VLSI for computation is investigated.