Description Usage Arguments Value References See Also
View source: R/feature_extraction.R
seq2feature_mds_large
extracts MDS features from a large number of
response processes. The algorithm proposed in Paradis (2018) is implemented with minor
variations to perform MDS. The algorithm first selects a relatively small subset of
response processes to perform the classical MDS. Then the coordinate of each of the
other response processes are obtained by minimizing the loss function related to the target
response processes and the those in the subset through BFGS.
1 2  seq2feature_mds_large(seqs, K, dist_type = "oss_action", subset_size,
subset_method = "random", n_cand = 10, pca = TRUE, L_set = 1:3)

seqs 
an object of class 
K 
the number of features to be extracted. 
dist_type 
a character string specifies the dissimilarity measure for two response processes. See 'Details'. 
subset_size 
the size of the subset on which classical MDS is performed. 
subset_method 
a character string specifying the method for choosing the subset.
It must be one of 
n_cand 
The size of the candidate set when selecting the subset. It is only used when

pca 
logical. If 
L_set 
length of ngrams considered 
seq2feature_mds_large
returns an n \times K matrix of extracted
features.
Paradis, E. (2018). Multidimensional Scaling with Very Large Datasets. Journal of Computational and Graphical Statistics, 27, 935–939.
Other feature extraction methods: aseq2feature_seq2seq
,
atseq2feature_seq2seq
,
seq2feature_mds
,
seq2feature_ngram
,
seq2feature_seq2seq
,
tseq2feature_seq2seq
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