R: Conditional ISOMAP

condIsomap {cml}

R Documentation

Conditional ISOMAP

Description

Finds a low-dimensional manifold embedding of a given distance/dissimilarity matrix, conditioning on available manifold information. The method applies conditional MDS (see condMDS) to a graph distance matrix computed for the given distances/dissimilarities, using the isomap{vegan} function.

Usage

condIsomap(d, V, u.dim, epsilon = NULL, k, 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 distance/dissimilarity matrix of N entities (or a `dist` object).
`V`	an Nxq matrix of q manifold auxiliary parameter values of the N entities.
`u.dim`	the embedding dimension.
`epsilon`	shortest dissimilarity retained.
`k`	Number of shortest dissimilarities retained for a point. If both `epsilon` and `k` are given, `epsilon` will be used.
`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.
`...`	other arguments for the `isomap{vegan}` function.

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 164, 148-152. https://doi.org/10.1016/j.patrec.2022.11.007

Examples

# see help(cml)

[Package cml version 0.2.2 Index]