nonlinear_DPPCA: Dual Probabilistic Principal Component Analysis

In Rdimtools: Dimension Reduction and Estimation Methods

Dual Probabilistic Principal Component Analysis

Description

Dual view of PPCA optimizes the latent variables directly from a simple Bayesian approach to model the noise using the multivariate Gaussian distribution of zero mean and spherical covariance \beta^{-1} I. When \beta is too small, the algorithm automatically returns an error and provides a guideline for minimal value that enables successful computation.

Usage

do.dppca(X, ndim = 2, beta = 1)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension (default: 2).
beta	the degree for modeling the level of noise (default: 1).

Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

algorithm

name of the algorithm.

References

\insertRef

lawrence_probabilistic_2005Rdimtools

See Also

do.ppca

Examples

## load iris data
data(iris)
X     = as.matrix(iris[,1:4])
lab   = as.factor(iris[,5])

## compare difference choices of 'beta'
embed1 <- do.dppca(X, beta=0.2)
embed2 <- do.dppca(X, beta=1)
embed3 <- do.dppca(X, beta=5)

## Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3), pty="s")
plot(embed1$Y , col=lab, pch=19, main="beta=0.2")
plot(embed2$Y , col=lab, pch=19, main="beta=1")
plot(embed3$Y , col=lab, pch=19, main="beta=5")
par(opar)

Rdimtools documentation built on Nov. 5, 2025, 5:28 p.m.