Computing a graph induced Fused LASSO Signal Approximator. You can use it to denoise data. The package includes some utility methods to assess the algorithms and apply it to images as well es ion-mobilty spectrometry (IMS) data sets.
For the one dimensional version of the Johnson's dynamic programming algorithm, have a look into Lasso.jl
The fused LASSO signal approximator can be used to denoise e.g. images:
Cleaned by FLSA
Fast Gradient Projection (FGP)
Also known as Fast Iterative Shrinkage Algorithm (FISTA).
Alternating Direction Method of Multipliers (ADMM)
Maximum Gap Tree (MGT)
Own algorithm based on a iterative approximation by dynamic programming algorithm minimizing a sub-tree-graph.
using FLSA graph = FLSA.img_graph(size(B)..., dn=2, lam=0.1) # (1) F = FLSA.fista(B, graph, verbose=true; max_iter=10) # (2)
First you have to define graph (line
Then one of the algorithms above are called (see
In order to be easily called from other languages a HDF5 intermediate data structure is supported that looks as follows (see generate_hdf5.py for a working python example):
1 2 3 ... n nodes/input /weight 1 2 3 ... m edges/head /tail /weight algorithm/@name /@param1 /@param2