A latent factor approach for prediction from multiple assays

07/16/2018
by   J. Kenneth Tay, et al.
0

In many domains such as healthcare or finance, data often come in different assays or measurement modalities, with features in each assay having a common theme. Simply concatenating these assays together and performing prediction can be effective but ignores this structure. In this setting, we propose a model which contains latent factors specific to each assay, as well as a common latent factor across assays. We frame our model-fitting procedure, which we call the "Sparse Factor Method" (SFM), as an optimization problem and present an iterative algorithm to solve it.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/06/2021

Factor Modelling for Clustering High-dimensional Time Series

We propose a new unsupervised learning method for clustering a large num...
research
12/17/2015

Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers

We propose a novel classification model for weak signal data, building u...
research
10/05/2020

Determining the Number of Factors in High-dimensional Generalised Latent Factor Models

As a generalisation of the classical linear factor model, generalised la...
research
09/20/2019

On Recovering Latent Factors From Sampling And Firing Graph

Consider a set of latent factors whose observable effect of activation i...
research
10/05/2015

A Common-Factor Approach for Multivariate Data Cleaning with an Application to Mars Phoenix Mission Data

Data quality is fundamentally important to ensure the reliability of dat...
research
12/05/2019

A note on identifiability conditions in confirmatory factor analysis

Recently, Chen, Li and Zhang have established simple conditions characte...
research
07/11/2012

Factored Latent Analysis for far-field tracking data

This paper uses Factored Latent Analysis (FLA) to learn a factorized, se...

Please sign up or login with your details

Forgot password? Click here to reset