77 research outputs found
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Clustering in high-dimensional spaces is nowadays a recurrent problem in many
scientific domains but remains a difficult task from both the clustering
accuracy and the result understanding points of view. This paper presents a
discriminative latent mixture (DLM) model which fits the data in a latent
orthonormal discriminative subspace with an intrinsic dimension lower than the
dimension of the original space. By constraining model parameters within and
between groups, a family of 12 parsimonious DLM models is exhibited which
allows to fit onto various situations. An estimation algorithm, called the
Fisher-EM algorithm, is also proposed for estimating both the mixture
parameters and the discriminative subspace. Experiments on simulated and real
datasets show that the proposed approach performs better than existing
clustering methods while providing a useful representation of the clustered
data. The method is as well applied to the clustering of mass spectrometry
data
Shop stewards’ leadership, left-wing activism and collective workplace union organisation
Providing an account of the dynamic interrelationship between shop steward leadership and membership interaction, Ralph Darlington focuses particular attention on the much-neglected crucial role that left-wing political activists can play in shaping the nature of collective workplace relations
Reversals of fortune: path dependency, problem solving, and temporal cases
Historical reversals highlight a basic methodological problem: is it possible to treat two successive periods both as independent cases to compare for causal analysis and as parts of a single historical sequence? I argue that one strategy for doing so, using models of path dependency, imposes serious limits on explanation. An alternative model which treats successive periods as contrasting solutions for recurrent problems offers two advantages. First, it more effectively combines analytical comparisons of different periods with narratives of causal sequences spanning two or more periods. Second, it better integrates scholarly accounts of historical reversals with actors’ own narratives of the past
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