condSmacof {cml}

Conditional SMACOF

Description

Conditional SMACOF algorithms. Intended for internal usage.

Usage

condSmacof(d, V, u.dim, W,
           method = c('matrix', 'vector'), exact = TRUE,
           it.max = 1000, gamma = 1e-05,
           init = c('none', 'eigen', 'user'),
           U.start, B.start)

Arguments

d

a dist object of N entities.

V

an Nxq matrix of q manifold auxiliary parameter values of the N entities.

u.dim

the embedding dimension.

W

an NxN symmetric weight matrix. If not given, a matrix of ones will be used.

method

if matrix, there are no restrictions for the B matrix . If vector, the B matrix is restricted to be diagonal. The latter is more efficient for large q.

exact

only relevant if W is not given. In this case, if exact == FALSE, U is updated by the large-N approximation formula.

it.max

the max number of conditional SMACOF iterations.

gamma

conditional SMACOF stops early if the reduction of normalized conditional stress is less than gamma

init

initialization method.

U.start

user-defined starting values for the embedding (when init = 'user')

B.start

starting B matrix.

Value

U

the embedding result.

B

the estimated B matrix.

stress

Normalized conditional stress value.

sigma

the conditional stress value at each iteration.

init

the value of the init argument.

U.start

the starting values for the embedding.

B.start

starting values for the B matrix.

method

the value of the method argument.

exact

the value of the exact argument.

Author(s)

Anh Tuan Bui

References

Bui, A.T. (2021). Dimension Reduction with Prior Information for Knowledge Discovery. arXiv:2111.13646. https://arxiv.org/abs/2111.13646.

Bui, A. T. (2022). A Closed-Form Solution for Conditional Multidimensional Scaling. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2022.11.007