Description Usage Arguments Details Value Author(s) References Examples
Estimates the intrinsic dimension of data using the Morisita estimator of intrinsic dimension.
1  MINDID(X, scaleQ=1:5, mMin=2, mMax=2)

X 
A N x E 
scaleQ 
A vector (at least two values). It contains the values of l^(1)
chosen by the user (by default: 
mMin 
The minimum value of m (by default: 
mMax 
The maximum value of m (by default: 
l is the edge length of the grid cells (or quadrats). Since the variables (and consenquently the grid) are rescaled to the [0,1] interval, l is equal to 1 for a grid consisting of only one cell.
l^(1) is the number of grid cells (or quadrats) along each axis of the Euclidean space in which the data points are embedded.
l^(1) is equal to Q^(1/E) where Q is the number of grid cells and E is the number of variables (or features).
l^(1) is directly related to delta (see References).
delta is the diagonal length of the grid cells.
A list of two elements:
a data.frame
containing the ln value of the mMorisita index for each value
of ln(delta) and m. The values of ln(delta) are provided with regard to the [0,1] interval.
a data.frame
containing the values of Sm and Mm for each value of m.
Jean Golay jeangolay@gmail.com
J. Golay and M. Kanevski (2015). A new estimator of intrinsic dimension based on the multipoint Morisita index, Pattern Recognition 48 (12):4070–4081.
J. Golay, M. Leuenberger and M. Kanevski (2017). Feature selection for regression problems based on the Morisita estimator of intrinsic dimension, Pattern Recognition 70:126–138.
J. Golay and M. Kanevski (2017). Unsupervised feature selection based on the Morisita estimator of intrinsic dimension, KnowledgeBased Systems 135:125134.
J. Golay, M. Leuenberger and M. Kanevski (2015). Morisitabased feature selection for regression problems. Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges (Belgium).
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[1] "The ID estimate is equal to 2.02"
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