Recent work has suggested that the prefrontal cortex (PFC) plays a key role in context-
dependent perceptual decision-making. In this study, we addressed that role using a new …

We propose a prenet (pr oduct-based e lastic net), a novel penalization method for factor
analysis models. The penalty is based on the product of a pair of elements in each row of the …

Human mortality data sets can be expressed as multiway data arrays, the dimensions of
which correspond to categories by which mortality rates are reported, such as age, sex …

We develop new statistical theory for probabilistic principal component analysis models in
high dimensions. The focus is the estimation of the noise variance, which is an important …

In this brief, we propose a fast expectation conditional maximization (ECM) algorithm for
maximum-likelihood (ML) estimation of mixtures of factor analyzers (MFA). Unlike the …

Mixture of t factor analyzers (MtFA) have been shown to be a sound model-based tool for
robust clustering of high-dimensional data. This approach, which is deemed to be one of …

Existing works on variational bayesian (VB) treatment for factor analysis (FA) model such as
[Ghahramani, Z., & Beal, M.(2000). Variational inference for Bayesian mixture of factor …

This work presents a generalization of classical factor analysis (FA). Each of M channels
carries measurements that share factors with all other channels, but also contains factors …

In the application of the popular maximum likelihood method to factor analysis, the number
of factors is commonly determined through a two-stage procedure, in which stage 1 performs …

This technical note proposes a novel profile likelihood method for estimating the covariance
parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer …