How to estimate the fractal dimension of each data point？
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I used the Em algorithm to perform point-dimensional estimation and estimated the following parameters.
The input is the distance between the data and the nearest point （20000×1)．
The output is
d: A 1 x M vector of dimension parameters
delta: A 1 x L (L = sum( [ m_1, m_2, .., m_M ] )) vecter of density parameters
prior_prob: A 1 x L vecter of base probabilities of clusters
conditional_prob: K x L matrix of conditional probability of cluster assignment given each data.
LLfinal: The log likelihood of the best model.
MapToDim: A binary M x L matrix indicating which density clusters (column) is assigned to each dimensional cluster (each row).
SettingsSearch: A struct variable containing meta information about the model search process.
How can I calculate the fractal dimension of all the data after finding the point dimension of each cluster?