• Corpus ID: 203902452

Perturbed factor analysis: Improving generalizability across studies

@article{Roy2019PerturbedFA,
  title={Perturbed factor analysis: Improving generalizability across studies},
  author={Arkaprava Roy and Isaac Lavine and Amy H. Herring and David B. Dunson},
  journal={arXiv: Methodology},
  year={2019}
}
Factor analysis is routinely used for dimensionality reduction. However, a major issue is `brittleness' in which one can obtain substantially different factors in analyzing similar datasets. Factor models have been developed for multi-study data by using additive expansions incorporating common and study-specific factors. However, allowing study-specific factors runs counter to the goal of producing a single set of factors that hold across studies. As an alternative, we propose a class of… 

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References

SHOWING 1-10 OF 27 REFERENCES

Bayesian multistudy factor analysis for high-throughput biological data

The proposed approach performs very well in a range of different scenarios, and outperforms standard Factor analysis in all the scenarios identifying replicable signal in unsupervised genomic applications.

BAYESIAN MODEL ASSESSMENT IN FACTOR ANALYSIS

This work explores reversible jump MCMC methods that build on sets of parallel Gibbs sampling-based analyses to generate suitable empirical proposal distributions and that address the challenging problem of finding efficient proposals in high-dimensional models.

Sparse Bayesian infinite factor models.

This work proposes a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases, and develops an efficient Gibbs sampler that scales well as data dimensionality increases.

Bayesian time-aligned factor analysis of paired multivariate time series

A Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) is proposed that includes uncertainty in time alignment through an unknown warping function and enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm.

Non-iterative Joint and Individual Variation Explained

This paper introduces Non-iterative Joint and Individual Variation Explained (Non-iteratives JIVE), capturing both joint and individual variation within each data block, which is robust against the heterogeneity among data blocks without a need for normalization.

JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.

JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

These case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers.

Bayesian Factorizations of Big Sparse Tensors

Taking a Bayesian approach, priors are placed on terms in the factorization and an efficient Gibbs sampler for posterior computation is developed and shown to have excellent performance in simulations and several real data applications.

Angle-based joint and individual variation explained