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sparsify_weights

Sparsify weights

Description

This function makes weights of a mixture model more sparse using gradient based penalty methods.

Usage

sparsify_weights(weight.matrix.proposed, weight.matrix.current = NULL, 
    penalty = "entropy", lambda = 0)

Arguments

weight.matrix.proposed

N \times K weight matrix
weight.matrix.current

N \times K weight matrix
penalty

type of penalty: c("entropy", "1-Lq", "lognorm"). Default: "entropy"
lambda

penalization parameter: larger lambda gives sparser mixture weights

See Also

Examples

WW <- matrix(c(rexp(10, 1/10), runif(10)), ncol = 5, byrow = FALSE)
WW <- normalize(WW)
WW_sparse <- sparsify_weights(WW, lambda = 0.1)
WW_more_sparse <- sparsify_weights(WW, lambda = 0.5)
compute_mixture_penalty(WW)
compute_mixture_penalty(WW_sparse)
compute_mixture_penalty(WW_more_sparse)
LICORS

Light Cone Reconstruction of States - Predictive State Estimation From Spatio-Temporal Data

v0.2.0
GPL-2
Authors
Georg M. Goerg <gmg@stat.cmu.edu>
Initial release
2013-11-20

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