This paper introduces a novel statistical latent class model for probabilistic grouping of distributional and histogram data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical applicati...

We present a maximum margin framework that clusters data using latent variables. Using latent representations enables our framework to model unobserved information embedded in the data. We implement our idea by large margin learning, and develop an alternating descent algorithm to effectively solve the resultant non-convex optimization problem. We instantiate our latent maximum margin clusterin...

Latent class analysis is a popular statistical learning approach. A major challenge for learning generalized latent class is the complexity in searching the huge space of models and parameters. The computational cost is higher when the model topology is more flexible. In this paper, we propose the notion of dominance which can lead to strong pruning of the search space and significant reduction...

Summary Usually in latent class (LC) analysis, external predictors are taken to be cluster conditional probability (LC models with predictors), and/or score regression models). In such cases, their distribution is not of interest. Class‐specific interest the distal outcome model, when variables assumed depend on LC membership. this paper, we consider a more general formulation, that embeds both...

Binary data latent class analysis is a form of model-based clustering applied in a wide range of fields. A central assumption of this model is that of conditional independence of responses given latent class membership, often referred to as the “local independence” assumption. The results of latent class analysis may be severely biased when this crucial assumption is violated; investigating the...

Co-clustering aims at simultaneously partitioning both dimensions of a data matrix. It has demonstrated better performances than one-sided clustering for high-dimensional data. The Latent Block Model (LBM) is probabilistic model co-clustering based on mixture models that proven useful broad class In this paper, we propose to leverage prior knowledge in the form pairwise semi-supervision row and...