- PCA
- ICA
- NMF
using DimensionalityReduction
srand(1)
X = hcat(10 * randn(10), randn(10))
cov(X)
theta = pi / 4.0
R = [cos(theta) -sin(theta); sin(theta) cos(theta)]
X = X * R
cov(X)
results = pca(X)
using DimensionalityReduction
# Generate true sources
S_true = rand(5,1000)
# Mixing matrix
H_true = randn(5, 5)
# generate observed signals
X = H_true*S_true
results = ica(X)
using DimensionalityReduction
X = hcat(eye(2), eye(2))
X = vcat(X, X, X, X)
results = nmf(X, 2)