seq2feature_mds_stochastic {ProcData}

Feature extraction by stochastic mds

Description

Feature extraction by stochastic mds

Usage

seq2feature_mds_stochastic(seqs = NULL, K = 2,
  dist_type = "oss_action", max_epoch = 100, step_size = 0.01,
  pca = TRUE, tot = 1e-06, return_dist = FALSE, L_set = 1:3)

Arguments

seqs

a "proc" object or a square matrix. If a squared matrix is provided, it is treated as the dissimilary matrix of a group of response processes.

K

the number of features to be extracted.

dist_type

a character string specifies the dissimilarity measure for two response processes. See 'Details'.

max_epoch

the maximum number of epochs for stochastic gradient descent.

step_size

the step size of stochastic gradient descent.

pca

a logical scalar. If TRUE, the principal components of the extracted features are returned.

tot

the accuracy tolerance for determining convergence.

return_dist

logical. If TRUE, the dissimilarity matrix will be returned. Default is FALSE.

L_set

length of ngrams considered.

Value

seq2feature_mds_stochastic returns a list containing

theta

a numeric matrix giving the K extracted features or principal features. Each column is a feature.

loss

the value of the multidimensional scaling objective function.

dist_mat

the dissimilary matrix. This element exists only if return_dist=TRUE.