matchFeat (version 1.0)

match.rec: Recursive Initialization Method

Description

RECUR1 algorithm of Bandelt et al (2004) to find starting point in the multidimensional assignment problem with decomposable costs (MDADC)

Usage

match.rec(x, unit = NULL, w = NULL, control = list())

Value

A list of class matchFeat with components

sigma

best set of permutations for feature vectors (\((m,n)\) matrix)

cluster

associated clusters (= inverse permutations)

cost

minimum objective value

mu

sample mean for each class/label (\((p,m)\) matrix)

V

sample covariance for each class/label (\((p,m)\) matrix

call

function call

Arguments

x

data: matrix of dimensions \((mn,p)\) or 3D array of dimensions \((p,m,n)\) with \(m\) = number of labels/classes, \(n\) = number of sample units, and \(p\) = number of variables)

unit

integer (=number of units) or vector mapping rows of x to sample units (length \(mn\)). Must be specified only if x is a matrix.

w

weights for loss function: single positive number, \(p\)-vector of length, or \((p,p)\) positive definite matrix

control

tuning parameters

References

Degras (2022) "Scalable feature matching across large data collections." tools:::Rd_expr_doi("10.1080/10618600.2022.2074429")
Bandelt, Maas, and Spieksma (2004), "Local search heuristics for multi-index assignment problems with decomposable costs." tools:::Rd_expr_doi("10.1057/palgrave.jors.2601723")

See Also

match.2x, match.bca, match.gaussmix, match.template, match.kmeans

Examples

data(optdigits)
m <- length(unique(optdigits$label)) # number of classes
n <- nrow(optdigits$x) / m # number of units

## Use function with data in matrix form
fit1 <- match.rec(optdigits$x, unit=n)

## Use function with data in array form
p <- ncol(optdigits$x)
x <- t(optdigits$x)
dim(x) <- c(p,m,n)
fit2 <- match.rec(x)