flsa_graph: Segmentation using graph structure and the fused lasso...

In goepp/graphseg: Segmentation of Graph-Based Signals

Segmentation using graph structure and the fused lasso estimate

Description

Wrapper around the function flsa::flsa, which computes the fused lasso signal approximator (see reference). Like agraph, this function takes a signal on graph and returns a clustering thereof into a piecewise-constant signal. The difference with agraph is the estimation method: agraph works well when the true signal is sparse and its computation time scales well to large graphs.

Usage

flsa_graph(gamma, graph, lambda)

Arguments

gamma	entry vector to regularize
graph	graph (an igraph object) giving the regularization structure
lambda	regularizing constant

Value

A list with the following elements:

• result: matrix whose rows are the segmented output of input signal gamma, for each value of lambda

• bic, gcv, and aic: vectors of length length(lambda), giving the BIC, GCV, and AIC criteria for each value of lambda. See references below.

• model_dim, nll: vectors of length length(lambda), giving the model dimension and negative log-likelihood for each value of lambda. See reference below for the definition of these terms.

References

Hoefling, H., A Path Algorithm for the Fused Lasso Signal Approximator, Journal of Computational and Graphical Statistics (2010) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/jcgs.2010.09208")}

See Also

graphseg::agraph()

goepp/graphseg documentation built on Nov. 23, 2023, 9:29 a.m.