riem.nmshift: Nonlinear Mean Shift In Riemann: Learning with Data on Riemannian Manifolds

Description

Given N observations X_1, X_2, …, X_N \in \mathcal{M}, perform clustering of the data based on the nonlinear mean shift algorithm. Gaussian kernel is used with the bandwidth h as of

G(x_i, x_j) \propto \exp ≤ft( - \frac{ρ^2 (x_i,x_j)}{h^2} \right)

where ρ(x,y) is geodesic distance between two points x,y\in\mathcal{M}. Numerically, some of the limiting points that collapse into the same cluster are not exact. For such purpose, we require maxk parameter to search the optimal number of clusters based on k-medoids clustering algorithm in conjunction with silhouette criterion.

Usage

 1 riem.nmshift(riemobj, h = 1, maxk = 5, maxiter = 50, eps = 1e-05) 

Arguments

 riemobj a S3 "riemdata" class for N manifold-valued data. h bandwidth parameter. The larger the h is, the more blurring is applied. maxk maximum number of clusters to determine the optimal number of clusters. maxiter maximum number of iterations to be run. eps tolerance level for stopping criterion.

Value

a named list containing

distance

an (N\times N) distance between modes corresponding to each data point.

cluster

a length-N vector of class labels.

References

\insertRef

subbarao_nonlinear_2009Riemann

Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 #------------------------------------------------------------------- # Example on Sphere : a dataset with three types # # class 1 : 10 perturbed data points near (1,0,0) on S^2 in R^3 # class 2 : 10 perturbed data points near (0,1,0) on S^2 in R^3 # class 3 : 10 perturbed data points near (0,0,1) on S^2 in R^3 #------------------------------------------------------------------- ## GENERATE DATA set.seed(496) ndata = 10 mydata = list() for (i in 1:ndata){ tgt = c(1, stats::rnorm(2, sd=0.1)) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } for (i in (ndata+1):(2*ndata)){ tgt = c(rnorm(1,sd=0.1),1,rnorm(1,sd=0.1)) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } for (i in ((2*ndata)+1):(3*ndata)){ tgt = c(stats::rnorm(2, sd=0.1), 1) mydata[[i]] = tgt/sqrt(sum(tgt^2)) } myriem = wrap.sphere(mydata) mylabs = rep(c(1,2,3), each=ndata) ## RUN NONLINEAR MEANSHIFT FOR DIFFERENT 'h' VALUES run1 = riem.nmshift(myriem, maxk=10, h=0.1) run2 = riem.nmshift(myriem, maxk=10, h=1) run3 = riem.nmshift(myriem, maxk=10, h=10) ## MDS FOR VISUALIZATION mds2d = riem.mds(myriem, ndim=2)$embed ## VISUALIZE opar <- par(no.readonly=TRUE) par(mfrow=c(2,3), pty="s") plot(mds2d, pch=19, main="label : h=0.1", col=run1$cluster) plot(mds2d, pch=19, main="label : h=1", col=run2$cluster) plot(mds2d, pch=19, main="label : h=10", col=run3$cluster) image(run1$distance[,30:1], axes=FALSE, main="distance : h=0.1") image(run2$distance[,30:1], axes=FALSE, main="distance : h=1") image(run3\$distance[,30:1], axes=FALSE, main="distance : h=10") par(opar) 

Riemann documentation built on June 20, 2021, 5:07 p.m.