Description Usage Arguments Value References
This implements the algorithm described in Optimal Manifold Representation of Data: An Information Theoretic Approach by Denis Chigirev and William Bialek, which attempts to reduce higher dimensional data onto a lower dimensional manifold (by default 1D).
1 2 3 | manifold_reduction(coords, lambda = 2, neighbourhood_size = 20L,
knntouse = 75L, no_iterations = 45L, maxDim = 1.2,
Verbose = TRUE, solvemethod = 0L)
|
coords |
d x N matrix of d-dimensional data points |
lambda |
A spatial parameter that determines the interactions between neighbouring points according to the tradeoff F(M,Pm) = D + lambda*I (see original paper for details). |
neighbourhood_size |
Number of nearest neighbours to consider when calculating local dimensionality (default 20). |
knntouse |
Number of nearest neighbours to consider when calculating interactions. |
no_iterations |
The number of iterations to use |
maxDim |
The local dimensionality below which points are fixed. The
default value of |
Verbose |
Whether to print status messages |
solvemethod |
An integer from -1 to 5 determining the solver used to compute the local dimensionality. |
a list with the following elements:
gamma the d x N coordinates of the output points on manifold
P
lambda determines the tradeoff F(M,Pm) = D + lambda*I
dimension the local dimensionality of the manifold
Optimal Manifold Representation of Data: An Information Theoretic Approach
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