Description Usage Arguments Details Value Author(s) References See Also Examples
Finds \mathrm{argmin}_{p\in M}∑_{i=1} ^ {N} ρ(d(p,y_i)) through a gradient descent algorithm.
1 2 3 4 5 6 7 8 9 | intrinsic_location(
manifold,
y,
estimator,
c = NULL,
p_tol = 1e-05,
V_tol = 1e-05,
max_iter = 1e+05
)
|
manifold |
Type of manifold ( |
y |
A matrix or data frame whose columns represent points on the manifold. |
estimator |
M-type estimator ( |
c |
Multiplier of σ, the square root of the variance, used in
the cutoff parameter for the |
p_tol |
Termination condition for the distance between consecutive
updates of |
V_tol |
Termination condition for the distance between columns of
consecutive updates of |
max_iter |
Maximum number of gradient descent steps before ending the algorithm. |
In the case of the 'sphere'
, an error will be raised if all points are
on a pair of antipodes.
A vector representing the location estimate
Ha-Young Shin
Fletcher, P. T. (2013). Geodesic regression and the theory of least squares on Riemannian manifolds. International Journal of Computer Vision, 105, 171-185.
Kim, H. J., Adluru, N., Collins, M. D., Chung, M. K., Bendin, B. B., Johnson, S. C., Davidson, R. J. and Singh, V. (2014). Multivariate general linear models (MGLM) on Riemannian manifolds with applications to statistical analysis of diffusion weighted images. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2705-2712.
Shin, H.-Y. and Oh H.-S. (2020). Robust Geodesic Regression. <arXiv:2007.04518>
geo_reg
, rbase.mean
,
rbase.median
.
1 2 | y <- matrix(runif(100, 1000, 2000), nrow = 10)
intrinsic_location('euclidean', y, 'l2')
|
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