The maximum‐likelihood solution in inter‐battery factor analysis

By Michael W. Browne. 1979

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In Statistics and Probability

Citations in the corpus

Beyond CCA: Moment Matching for Multi-View Models (2016, background): “Importantly, the joint diagonalization problem we deal with in this paper is conceptually different from the one considered, e.g., by Kuleshov et al. (2015) (and references therein) and, therefore, the respective algorithms are not applicable here.”
Bayesian group factor analysis with structured sparsity (2016, methodology): “This model was originally called inter-battery factor analysis (IBFA) (Browne, 1979) and recently has been studied under a full Bayesian inference framework (Klami et al., 2013).”
All-but-the-Top: Simple and Effective Postprocessing for Word Representations (2017, methodology): “While CCA-based spectral factorization methods have long been understood from a probabilistic (i.e., generative model) view point (Browne, 1979; Hotelling, 1936) and recently in the NLP context (Stratos et al., 2015), a corresponding effort for the PMI-based methods has only recently been conducted…”
A Survey of Multi-View Representation Learning (2016, unknown): “In addition, Browne [66] also proves that the maximum likelihood solution of Eq.”
On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, methodology): “Jointly, these results empirically substantiate that the two approaches to SEM are neither the same nor interchangeable, though transformations have been found to turn canonical correlation results into maximum likelihood estimates for the inter-battery factor model (Browne, 1979; Tucker, 1958), or ”
Bayesian Canonical correlation analysis (2013, methodology): “…the generative formulation is equivalent to classical CCA in the sense that they both find the same subspace, one difference pointed out also by Browne (1979) is worth emphasizing: The generative formulation maintains a single latent variable z that captures the shared variation, whereas CCA…”