GsvdInitialization

This package implements the technique in the paper GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization. It is used to recover Non-negative matrix factorization(NMF) components from an initial lower-rank factorization by exploiting the generalized singular value decomposition (GSVD) between existing NMF results and the SVD of X. This method allows the incremental expansion of the number of components, which can be convenient and effective for interactive analysis of large-scale data.

See also NMFMerge for the converse operation. Together, the two result in a substantial improvement in the quality and consistency of NMF factorization.

Demo:

To run this demo, NMF.jl and LinearAlgebra.jl are also required.

Install and load packages (type ] at the julia> prompt to enter pkg> mode): julia pkg> add GsvdInitialization; julia> using GsvdInitialization, NMF, LinearAlgebra;

Generating grouth truth with 10 features.

julia julia> include("demo/generate_ground_truth.jl") julia> W_GT, H_GT = generate_ground_truth(); julia> X = W_GT*H_GT;

Running standard NMF(HALS) using NNDSVD as initialization on X. Here, we're taking a couple of precautions to try to ensure the best possible result from NMF: - we disable premature convergence by setting maxiter to something that is practically infinite - we use the full svd, rather than rsvd, for initializing NNDSVD, as svd gives higher-quality results than rsvd Despite these precautions, we'll see that the NMF result leaves much to be desired:

julia julia> result_hals = nnmf(X, 10; init=:nndsvd, alg = :cd, tol = 1e-4, maxiter=10^12, initdata = svd(X)); julia> sum(abs2, X-result_hals.W*result_hals.H)/sum(abs2, X) 0.0999994991270576 The result is given by

This factorization is not perfect as two components are the same and two features share one component. Then, running GSVD-NMF on X (also using NNSVD as initialization) and computing the new reconstruction error:

julia Wgsvd, Hgsvd = gsvdnmf(X, 9=>10; alg = :cd, tol_final = 1e-4, tol_intermediate = 1e-2, maxiter = 10^12); julia> sum(abs2, X-Wgsvd*Hgsvd)/sum(abs2, X) 1.2322603074132593e-10 An imperfect factorization from nnmf alone was augmented by gsvdnmf to a perfect factorization. Here are the new components:

Functions

W, H = gsvdnmf(X::AbstractMatrix, ncomponents::Pair{Int,Int}; tolfinal=1e-4, tolintermediate=1e-4, kwargs...)

Perform "GSVD-NMF" on the data matrix X.

Arguments:

• X: non-negative data matrix

• ncomponents: in the form of n1 => n2, augments from n1 components to n2components, where n1 is the number of components for initial NMF (under-complete NMF), and n2 is the number of components for final NMF.

Alternatively, ncomponents can be an integer denoting the number of components for final NMF. In this case, gsvdnmf defaults to augment components on initial NMF solution by 1.

Keyword arguments:

• tol_final: The tolerence of final NMF, default:10^{-4}

• tol_intermediate: The tolerence of initial NMF (under-complete NMF), default: tol_final

Other keyword arguments are passed to NMF.nnmf.

W, H = gsvdnmf(X::AbstractMatrix, W::AbstractMatrix, H::AbstractMatrix, f; n2 = size(first(f), 2), tol_nmf=1e-4, kwargs...)

Augment W and H to have n2 components, subsequently polished by NMF.

Arguments:

• X: non-negative data matrix

• W and H: initial NMF factorization

• n2: the number of components in augmented factorization

• f: SVD (or Truncated SVD) of X

Keyword arguments:

• tol_nmf: the tolerance of NMF polishing step, default: 1e-4

Other keyword arguments are passed to NMF.nnmf.

Wadd, Hadd, S = gsvdrecover(X, W0, H0, kadd, f)

Augment components for W and H without polishing by NMF.

Outputs:

Wadd: augmented NMF solution

Hadd: augmented NMF solution

S: generalized singular values for the kadd augmented components

Arguments:

X: non-nagetive 2D data matrix

W0: NMF solution

H0: NMF solution

kadd: number of new components

f: SVD (or Truncated SVD) of X

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