tempted - Temporal Tensor Decomposition, a Dimensionality Reduction Tool
for Longitudinal Multivariate Data
TEMPoral TEnsor Decomposition (TEMPTED), is a dimension
reduction method for multivariate longitudinal data with
varying temporal sampling. It formats the data into a temporal
tensor and decomposes it into a summation of low-dimensional
components, each consisting of a subject loading vector, a
feature loading vector, and a continuous temporal loading
function. These loadings provide a low-dimensional
representation of subjects or samples and can be used to
identify features associated with clusters of subjects or
samples. TEMPTED provides the flexibility of allowing subjects
to have different temporal sampling, so time points do not need
to be binned, and missing time points do not need to be
imputed.