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RMT and matrix completion

This example examines noisy matrix completion (estimating a low-rank matrix from noisy data with missing measurements) through the lens of random matrix theory, using the Julia language.

This page comes from a single Julia file: complete1.jl.

You can access the source code for such Julia documentation using the 'Edit on GitHub' link in the top right. You can view the corresponding notebook in nbviewer here: complete1.ipynb, or open it in binder here: complete1.ipynb.

Add the Julia packages that are need for this demo. Change false to true in the following code block if you are using any of the following packages for the first time.

if false
    import Pkg
    Pkg.add([
        "InteractiveUtils"
        "LaTeXStrings"
        "LinearAlgebra"
        "MIRTjim"
        "Plots"
        "Random"
        "StatsBase"
    ])
end

Tell Julia to use the following packages for this example. Run Pkg.add() in the preceding code block first, if needed.

using InteractiveUtils: versioninfo
using LaTeXStrings
using LinearAlgebra: Diagonal, dot, norm, rank, svd, svdvals
using MIRTjim: prompt, jim
using Plots: default, gui, plot, plot!, scatter!, savefig
using Plots.PlotMeasures: px
using Random: seed!
using StatsBase: mean, var
default(markerstrokecolor=:auto, label="", widen=true, markersize = 6,
 labelfontsize = 24, legendfontsize = 18, tickfontsize = 14, linewidth = 3,
)
seed!(0)
Random.TaskLocalRNG()

The following line is helpful when running this file as a script; this way it will prompt user to hit a key after each image is displayed.

isinteractive() && prompt(:prompt);

Helper functions

Generate random data for one trial:

function gen1(
    θ::Real = 3,
    M::Int = 100,
    N::Int = 2M,
    p_obs::Real = 1, # probability an element is observed
    T::Type{<:Real} = Float32,
)
    mask = rand(M, N) .<= p_obs
    u = rand((-1,+1), M) / T(sqrt(M)) # Bernoulli just for variety
    v = rand((-1,+1), N) / T(sqrt(N))
    # u = randn(T, M) / T(sqrt(M))
    # v = randn(T, N) / T(sqrt(N))
    X = θ * u * v' # theoretically rank-1 matrix
    Z = randn(T, M, N) / T(sqrt(N)) # gaussian noise
    Y = mask .* (X + Z) # missing entries set to zero
    return Y, u, v, θ, p_obs
end;

SVD results for 1 trial:

function trial1(args...)
    Y, u, v, θ, p_obs = gen1(args...)
    fac = svd(Y)
    σ1 = fac.S[1]
    u1 = fac.U[:,1]
    v1 = fac.Vt[1,:]
    return [σ1, abs2(dot(u1, u)), abs2(dot(v1, v))]
end;

Average nrep trials:

trial2(nrep::Int, args...) = mean((_) -> trial1(args...), 1:nrep);

SVD for each of multiple trials, for different SNRs and matrix sizes:

if !@isdefined(vgrid)

    # Simulation parameters
    T = Float32
    p_obs = 0.49
    Mlist = [30, 300]
    θmax = 4
    nθ = θmax * 4 + 1
    nrep = 100
    θlist = T.(range(0, θmax, nθ));
    labels = map(n -> latexstring("\$M = $n\$"), Mlist)

    c = 0.7 # non-square matrix to test
    c4 = c^0.25
    tmp = ((θ, M) -> trial2(nrep, θ, M, ceil(Int, M/c) #= N =#, p_obs)).(θlist, Mlist')
    σgrid = map(x -> x[1], tmp)
    ugrid = map(x -> x[2], tmp)
    vgrid = map(x -> x[3], tmp)
end;

Results

Compare theory predictions and empirical results. There is again notable agreement between theory and empirical results here.

σ1 plot

colors = [:orange, :red]
θfine = range(0, 2θmax, 60θmax + 1)
θmod = θfine .* sqrt(p_obs) # key modification from RMT!
sbg(θ) = θ > c4 ? sqrt((1 + θ^2) * (c + θ^2)) / θ : 1 + √(c)
stheory = sbg.(θmod) * sqrt(p_obs) # note modification!
bm = s -> "\\mathbf{\\mathit{$s}}"
ylabel = latexstring("\$σ_1($(bm(:Y)))\$ (Avg)")
ps = plot(θfine, stheory, color=:blue, label="theory",
    aspect_ratio = 1, legend = :topleft,
    xaxis = (L"θ", (0,θmax), 0:θmax),
    yaxis = (ylabel, (1,θmax), 1:θmax),
    annotate = (3.1, 3.6, latexstring("c = $c"), :left),
)
scatter!(θlist, σgrid[:,1], marker=:square, color=colors[1], label = labels[1])
scatter!(θlist, σgrid[:,2], marker=:circle, color=colors[2], label = labels[2])
plot!(θlist, θlist * p_obs, label=L"p \; θ", color=:black, linewidth=2,
    annotate = (3.1, 3.3, latexstring("p = $p_obs"), :left))
Example block output
prompt()

u1 plot

ubg(θ) = (θ > c4) ? 1 - c * (1 + θ^2) / (θ^2 * (θ^2 + c)) : 0
utheory = ubg.(θmod)
ylabel = latexstring("\$|⟨\\hat{$(bm(:u))}, $(bm(:u))⟩|^2\$ (Avg)")
pu = plot(θfine, utheory, color=:blue, label="theory",
    left_margin = 10px, legend = :bottomright,
    xaxis = (L"θ", (0,θmax), 0:θmax),
    yaxis = (ylabel, (0,1), 0:0.5:1),
)
scatter!(θlist, ugrid[:,1], marker=:square, color=colors[1], label = labels[1])
scatter!(θlist, ugrid[:,2], marker=:circle, color=colors[2], label = labels[2])
Example block output
prompt()

v1 plot

vbg(θ) = (θ > c^0.25) ? 1 - (c + θ^2) / (θ^2 * (θ^2 + 1)) : 0
vtheory = vbg.(θmod)
ylabel = latexstring("\$|⟨\\hat{$(bm(:v))}, $(bm(:v))⟩|^2\$ (Avg)")
pv = plot(θfine, vtheory, color=:blue, label="theory",
    left_margin = 10px, legend = :bottomright,
    xaxis = (L"θ", (0,θmax), 0:θmax),
    yaxis = (ylabel, (0,1), 0:0.5:1),
)
scatter!(θlist, vgrid[:,1], marker=:square, color=colors[1], label = labels[1])
scatter!(θlist, vgrid[:,2], marker=:circle, color=colors[2], label = labels[2])
Example block output
prompt()


if false
 savefig(ps, "complete1-s.pdf")
 savefig(pu, "complete1-u.pdf")
 savefig(pv, "complete1-v.pdf")
 pp = plot(ps, pu, pv; layout=(3,1), size=(600, 900))
end

Image example

Apply an SVD-based matrix completion approach to some noisy and incomplete image data.

Latent matrix

Make a matrix that has low rank:

tmp = [
    zeros(1,20);
    0 1 0 0 0 0 1 0 0 0 1 1 1 1 0 1 1 1 1 0;
    0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0;
    0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0;
    0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 1 1 0;
    zeros(1,20)
]';
rank(tmp)
4

Turn it into an image:

Xtrue = kron(10 .+ 80*tmp, ones(9,9))
rtrue = rank(Xtrue)
5

plots with consistent size

jim1 = (X ; kwargs...) -> jim(X; size = (700,300),
 leftmargin = 10px, rightmargin = 10px, kwargs...);

and consistent display range

jimc = (X ; kwargs...) -> jim1(X; clim=(0,99), kwargs...);

and with NRMSE label

nrmse = (Xh) -> round(norm(Xh - Xtrue) / norm(Xtrue) * 100, digits=1)
args = (xaxis = false, yaxis = false, colorbar = :none) # book
args = (;) # web
jime = (X; kwargs...) -> jimc(X; xlabel = "NRMSE = $(nrmse(X)) %",
 args..., kwargs...,
)
title = latexstring("\$$(bm(:X))\$ : Latent image")
pt = jimc(Xtrue; title, xlabel = " ", args...)
Example block output

Noisy / incomplete data

seed!(0)
p_see = 0.8
mask = rand(Float32, size(Xtrue)) .<= p_see
sigZ = 6
Mx,Nx = sort(collect(size(Xtrue)))
Z = sigZ * randn(size(Xtrue)) # AWGN
Y = mask .* (Xtrue + Z);

title = latexstring("\$$(bm(:Y))\$ : Corrupted image matrix\n(missing pixels set to 0)")
py = jime(Y ; title)
Example block output

Show mask; count proportion of missing entries

frac_nonzero = count(mask) / length(mask)
title = latexstring("\$$(bm(:M))\$ : Locations of observed entries")
pm = jim1(mask; title, args...,
    xlabel = "sampled fraction = $(round(frac_nonzero * 100, digits=1))%")
Example block output

Singular values.

The first 3 singular values of $Y$ are well above the "noise floor" caused by masking, but, relative to those of $X$ they are scaled down by a factor of $p$ as expected.

We also show the critical value of $σ$ where the phase transition occurs. $σ₄(X)$ is just barely above the threshold, and $σ₅(X)$ below the threshold, so we cannot expect a simple SVD approach to recover them.

c_4 = (Mx / Nx)^(1/4)
σcrit = sigZ^2 * sqrt(Nx) * c_4 / sqrt(p_see) # from RMT

pg = plot([1, Nx], [1, 1] * σcrit, color=:cyan,
 title="singular values",
 xaxis=(L"k", (1, Mx), [1, 3, 6, Mx]),
 yaxis=(L"σ_k",),
 leftmargin = 15px, bottommargin = 20px, size = (600,350), widen = true,
)
sv_x = svdvals(Xtrue)
sv_y = svdvals(Y)
scatter!(pg, sv_x, color=:blue, label="Xtrue", marker=:utriangle)
scatter!(pg, sv_y, color=:red, label="Y (data)", marker=:dtriangle)
scatter!(pg, sv_y[1:3] / p_see, color=:green, label="Y/p", marker=:hex, alpha=0.8)
Example block output
prompt()

Low-rank estimate

A simple low-rank estimate of $X$ from the first few SVD components of $Y$ works just so-so here. A simple SVD approach recovers the first 3 components well, but cannot estimate the 4th and 5th components.

r = 3
U,s,V = svd(Y)
s ./= p_see # correction for masking effect
Xr = U[:,1:r] * Diagonal(s[1:r]) * V[:,1:r]'
title = latexstring("Rank $r approximation of data \$$(bm(:Y))\$")
pr = jime(Xr ; title)
Example block output

How well do the singular vectors match? The first 3 components match quite well:

[sum(svd(Xr).U[:,1:r] .* svd(Xtrue).U[:,1:r], dims=1).^2;
 sum(svd(Xr).V[:,1:r] .* svd(Xtrue).V[:,1:r], dims=1).^2]
2×3 Matrix{Float64}:
 0.989971  0.933669  0.932252
 0.995877  0.967441  0.959262

The next 2 components match very poorly, as predicted:

[sum(svd(Y).U[:,4:5]/p_see .* svd(Xtrue).U[:,4:5], dims=1).^2;
 sum(svd(Y).V[:,4:5]/p_see .* svd(Xtrue).V[:,4:5], dims=1).^2]
2×2 Matrix{Float64}:
 0.014361   0.0215999
 0.0246448  0.0213955

Reproducibility

This page was generated with the following version of Julia:

using InteractiveUtils: versioninfo
io = IOBuffer(); versioninfo(io); split(String(take!(io)), '\n')
11-element Vector{SubString{String}}:
 "Julia Version 1.11.2"
 "Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)"
 "Build Info:"
 "  Official https://julialang.org/ release"
 "Platform Info:"
 "  OS: Linux (x86_64-linux-gnu)"
 "  CPU: 4 × AMD EPYC 7763 64-Core Processor"
 "  WORD_SIZE: 64"
 "  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)"
 "Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)"
 ""

And with the following package versions

import Pkg; Pkg.status()
Status `~/work/book-la-demo/book-la-demo/docs/Project.toml`
  [6e4b80f9] BenchmarkTools v1.5.0
  [aaaa29a8] Clustering v0.15.7
  [35d6a980] ColorSchemes v3.27.1
⌅ [3da002f7] ColorTypes v0.11.5
⌃ [c3611d14] ColorVectorSpace v0.10.0
⌅ [717857b8] DSP v0.7.10
  [72c85766] Demos v0.1.0 `~/work/book-la-demo/book-la-demo`
  [e30172f5] Documenter v1.8.0
  [4f61f5a4] FFTViews v0.3.2
  [7a1cc6ca] FFTW v1.8.0
  [587475ba] Flux v0.15.2
  [a09fc81d] ImageCore v0.10.5
  [71a99df6] ImagePhantoms v0.8.1
  [b964fa9f] LaTeXStrings v1.4.0
  [7031d0ef] LazyGrids v1.0.0
  [599c1a8e] LinearMapsAA v0.12.0
  [98b081ad] Literate v2.20.1
  [7035ae7a] MIRT v0.18.2
  [170b2178] MIRTjim v0.25.0
  [eb30cadb] MLDatasets v0.7.18
  [efe261a4] NFFT v0.13.5
  [6ef6ca0d] NMF v1.0.3
  [15e1cf62] NPZ v0.4.3
  [0b1bfda6] OneHotArrays v0.2.6
  [429524aa] Optim v1.10.0
  [91a5bcdd] Plots v1.40.9
  [f27b6e38] Polynomials v4.0.12
  [2913bbd2] StatsBase v0.34.4
  [d6d074c3] VideoIO v1.1.1
  [b77e0a4c] InteractiveUtils v1.11.0
  [37e2e46d] LinearAlgebra v1.11.0
  [44cfe95a] Pkg v1.11.0
  [9a3f8284] Random v1.11.0
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated`

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