tdenoise

This function constructs a de-noised version of the temporal tensor
using the low-rank components obtained by <code>svd_centralize</code> <code>tempted</code> and uses the loadings to

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.

Pixu Shi

tempted

Temporal Tensor Decomposition, a Dimensionality Reduction Tool
for Longitudinal Multivariate Data

tdenoise function

<dl><dt>res_tempted</dt>
<dd>Output of tempted</dd>
<dt>mean_svd</dt>
<dd>Output of svd_centralize</dd></dl>

Arguments

Calculate the de-noised temporal tensor — tdenoise

<dl>

<dt>res_tempted</dt>
<dd>Output of tempted</dd>


<dt>mean_svd</dt>
<dd>Output of svd_centralize</dd>

</dl>

tdenoise: Calculate the de-noised temporal tensor

Description

Usage

Value

Arguments