12 citations · 31 across the 9 of their papers we have counts for
5 papers · 1 filter
Scalable Regularised Joint Mixture Models
Thomas Lartigue, Sach Mukherjee
In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. When predictive models are applied to such data the…
On unsupervised projections and second order signals
Thomas Lartigue, Sach Mukherjee
Linear projections are widely used in the analysis of high-dimensional data. In unsupervised settings where the data harbour latent classes/clusters, the question of whether class…
Ancestral causal learning in high dimensions with a human genome-wide application
Umberto Noè, Bernd Taschler, Joachim Täger +2
We consider learning ancestral causal relationships in high dimensions. Our approach is driven by a supervised learning perspective, with discrete indicators of causal relationship…
Model-based clustering in very high dimensions via adaptive projections
Bernd Taschler, Frank Dondelinger, Sach Mukherjee
Mixture models are a standard approach to dealing with heterogeneous data with non-i.i.d. structure. However, when the dimension is large relative to sample size and where…
Network-based clustering with mixtures of L1-penalized Gaussian graphical models: an empirical investigation
Steven M. Hill, Sach Mukherjee
In many applications, multivariate samples may harbor previously unrecognized heterogeneity at the level of conditional independence or network structure. For example, in cancer bi…