This toolbox is no longer supported by its author. The code is working on the last 2015 Matlab version, but may not be compatible with future versions. Consider using pyRiemann as a (python) alternative.
Fee free to fork the toolbox and start your own support :)
This toolbox contain a set of matlab functions dedicated to covariance matrices estimation and manipulation. The key functions mainly focus on Riemanian geometry of SPD matrices, with distance, geodesic, tangent space and mean estimation of covariance matrices under different metrics.
This toolbox is licenced under GPLv3.
installer- Generate a set of SPD matrices according to a wishart distribution :
[COV, Sig] = generate_wishart_set(N,I,Df,Sig)
- Distance between two covariance matrices (by default euclidean metric) :
d = distance(C1,C2,metric) - Kullback-Leibler distance :
d = distance_kullback(C1,C2) - Log-euclidean distance :
d = distance_logeuclid(C1,C2) - Riemannian distance :
d = distance_riemann(C1,C2) - Optimal transportation distance :
d = distance_opttransp(C1,C2) - Log Determinant distance :
d = distance_ld(C1,C2)
- SCM
- Fixed point
- Normalized SCM
- MCD
- Set of covariance matrices for a set of 3D signals X : Nchannels x Tsamples x Ntrials
COV = covariances(X)
- Geodesic between two covariance matrices (by default euclidean metric) :
Ct = geodesic(C1,C2,t,metric) - Euclidean geodesic :
Ct = euclidean_geodesic(C1,C2,t) - Log-euclidean geodesic :
Ct = logeuclidean_geodesic(C1,C2,t) - Riemannian geodesic :
Ct = riemann_geodesic(C1,C2,t) - Optimal transpotation geodesic :
Ct = opttransp_geodesic(C1,C2,t)
- Mean of a set of covariances matrices ( by default euclidean metric) :
C = mean_covariances(COV,metric) - Riemannian mean :
C = riemann_mean(COV) - Riemannian median :
C = riemann_median(COV) - Riemannian trimmed mean (excluding outliers) :
C = riemann_trimmed_mean(COV) - Log-euclidean mean :
C = logeuclid_mean(COV) - Optimal transportation mean :
C = opttransp_mean(COV) - Log Determinant mean :
C = logdet_mean(COV) - Geodesic iterative mean :
C = geodesic_mean(COV,metric)
- Canonical logarithm of a covariance matrix :
lC = logm(C) - Canonical exponential of a covariance matrix :
eC = expm(C) - Riemannian logaritmic map :
S = RiemannLogMap(C) - Riemannian exponential map :
C = RiemannExpMap(S) - Tangent space mapping of a set of covariance matrices :
T = Tangent_space(COV,C) - Manifold mapping of a set of tangent vectors :
COV = UnTangent_space(T,C) - Tangent vector of a covariance matrix :
t = tangent_vector(C1,C) - Geodesic filtering of a set of covariance matrices :
COVf = geodesic_filter(COV,C,W)
- plot the manifold of a set of 2x2 covariance matrices :
manifold_plot(COV,label,boundary)
- minimum distance to mean :
Ytest = mdm(COVtest,COVtrain,Ytrain) - minimum distance to mean + geodesic filtering :
Ytest = fgmdm(COVtest,COVtrain,Ytrain) - kmeans usupervised classification :
Ytest = kmeanscov(COVtest,COVtrain,Nclass) - Tangent space logistic regression (soon) :
Ytest = tsglm(COVtest,COVtrain,Ytrain)
- Tangent space regularized LDA :
Ytest = tslda(COVtest,COVtrain,Ytrain) - Tangent space SVM (soon) :
Ytest = tssvm(COVtest,COVtrain,Ytrain)
% generate a wishart set of 10 5x5 covariances matrices with a degree of freedom equal to 11
COV = generate_wishart_set(5,10,11);
% estimate the Riemannian mean of this set.
C = mean_covariances(COV,'riemann')
C =
14.4625 1.4332 -3.7638 -2.0052 14.2517
1.4332 11.5863 -2.2292 7.7445 8.8240
-3.7638 -2.2292 24.4896 -0.3460 -3.9808
-2.0052 7.7445 -0.3460 12.1740 6.2503
14.2517 8.8240 -3.9808 6.2503 37.4416
% generate a set of trials , 5 channels, 100 time sample and 1000 trials
X = randn(5,100,1000);
% covariance matrix of each trial
COV = covariances(X);
% Riemannian mean
C = mean_covariances(COV,'riemann')
C =
0.9699 0.0012 0.0026 0.0050 0.0040
0.0012 0.9659 -0.0037 0.0059 0.0001
0.0026 -0.0037 0.9712 -0.0009 -0.0024
0.0050 0.0059 -0.0009 0.9687 -0.0034
0.0040 0.0001 -0.0024 -0.0034 0.9671
see example folder