dis_lpp {mlmts} | R Documentation |
Constructs a pairwise distance matrix based on locality preserving projections (LPP)
Description
dis_lpp
returns a pairwise distance matrix based on the
dissimilarity introduced by Weng and Shen (2008).
Usage
dis_lpp(X, approach = 1, k = 2, t = 1, features = FALSE)
Arguments
X |
A list of MTS (numerical matrices). |
approach |
Parameter indicating whether the feature vector representing
each MTS is constructed by means of Li's first ( |
k |
Number of neighbors determining the construction of the local
structure matrix |
t |
Parameter determining the construction of the local
structure matrix |
features |
Logical. If |
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
d_{LPP}(\boldsymbol X_T, \boldsymbol Y_T)=
\big| \big| {\boldsymbol \varphi^{\boldsymbol X_T}
\boldsymbol A_{LPP} - \boldsymbol \varphi^{\boldsymbol Y_T} \boldsymbol A_{LPP}} \big| \big|,
where \boldsymbol \varphi^{\boldsymbol X_T}
and \boldsymbol \varphi^{\boldsymbol Y_T}
are the feature
vectors constructed from Li's first (approach=1
) or Li's second (approach=2
)
approach with respect to series \boldsymbol X_T
and \boldsymbol Y_T
, respectively
and \boldsymbol A_{LPP}
is the matrix of locality preserving projections
whose columns are eigenvectors solving the generalized eigenvalue problem defined
by matrix \boldsymbol S
.
Value
If features = FALSE
(default), returns a distance matrix based on the distance d_{QCD}
. Otherwise, the function
returns a dataset of feature vectors, i.e., each row in the dataset contains the features
resulting from applying Li's first (approach=1
) or Li's second (approach=2
).
Author(s)
Ángel López-Oriona, José A. Vilar
References
Weng X, Shen J (2008). “Classification of multivariate time series using locality preserving projections.” Knowledge-Based Systems, 21(7), 581–587.
Examples
toy_dataset <- AtrialFibrillation$data[1 : 10] # Selecting the first 10 MTS from the
# dataset AtrialFibrillation
distance_matrix <- dis_lpp(toy_dataset) # Computing the pairwise
# distance matrix based on the distance dis_lpp
feature_dataset <- dis_lpp(toy_dataset, features = TRUE) # Computing
# the corresponding dataset of features