Description Usage Arguments Value Author(s) Examples
Uses the k nearest neighbours of observation x. From those observations the most dense [k*alpha] observations are used to provide a projection onto the [k*alpha] oberservations. The orthogonal distance to this projection space and the score distance within this projection space, based on the covariance of the [k*alpha] observations are calculated for each observation from the dataset.
1 | getLocalDescription(data, center, dist=NULL, k=20, alpha=0.5)
|
data |
A dataset, with n rows and p variables. Any matrix or data frame can be handed over. |
center |
An index of data, which defines the observation initiating the projection process. |
dist |
A distance matrix of data. If no distance matrix is provided, it will be calculated using dist(data). The parameter is used in order to make the calculation more efficient. |
k=20 |
the number of nearest neighbours considered. k further influences the dimension of the projection space [k*alpha] at the same time. |
alpha |
The robustness parameter for the estimation of the covariance matrix. It further influences the dimension of the projection space. |
od |
A vector of orthogonal distances of each observation with respect to the space spanned by the core observations. |
cd |
A vector of core distances of each observation with respect to the covariance structure defined by the core observations. |
ecd |
A vector of Euclidean core distances of each observation. |
core |
The indices of [k*alpha] observations used to span the projection space and estimate the covariance matrix. |
knn |
The k nearest neighbours of the center observation. |
center |
The observation initiating the local description. |
Thomas Ortner (thomas.ortner@tuwien.ac.at)
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