dis_mahalanobis_dtw {mlmts} | R Documentation |
Constructs a pairwise distance matrix based on a dissimilarity combining both the dynamic time warping and the Mahalanobis distance.
Description
dis_mahalanobis_dtw
returns a pairwise distance matrix based on a
dynamic time warping distance in which the local cost matrix is computed
by using the Mahalanobis distance (Mei et al. 2015).
Usage
dis_mahalanobis_dtw(X, M = NULL, ...)
Arguments
X |
A list of MTS (numerical matrices). |
M |
The matrix with respect to compute the Mahalanobis distance (default is the covariance matrix of concatenation of all MTS objects by rows). |
... |
Additional parameters for the function. See |
Details
Given a collection of MTS, the function returns the pairwise distance matrix,
where the distance between two MTS \boldsymbol X_T
and \boldsymbol Y_T
is defined as
a dynamic time warping-type distance in which the local cost matrix is
constructed by using the Mahalanobis distance.
Value
The computed pairwise distance matrix.
Author(s)
Ángel López-Oriona, José A. Vilar
References
Mei J, Liu M, Wang Y, Gao H (2015). “Learning a mahalanobis distance-based dynamic time warping measure for multivariate time series classification.” IEEE transactions on Cybernetics, 46(6), 1363–1374.
See Also
dis_dtw_1
, dis_dtw_2
, dis_mahalanobis_dtw
Examples
toy_dataset <- Libras$data[1 : 10] # Selecting the first 10 MTS from the
# dataset Libras
distance_matrix <- dis_mahalanobis_dtw(toy_dataset) # Computing the pairwise
# distance matrix based on the distance dis_mahalanobis_dtw