CumulantsFeatures.jl

Cumulants based features selection and outlier detection
Author iitis
Popularity
3 Stars
Updated Last
6 Months Ago
Started In
September 2018

CumulantsFeatures.jl

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CumulantsFeatures.jl uses multivariate cumulants to provide the algorithms for the outliers detection and the features selection given the multivariate data represented in the form of t x n matrix of Floats, t numerates the realisations, while n numerates the marginals.

Requires SymmetricTensors.jl Cumulants.jl and CumulantsUpdates.jl to compute and update multivariate cumulants of data.

As of 24/09/2018 @kdomino is the lead maintainer of this package.

Installation

Within Julia, run

pkg> add CumulantsFeatures

Parallel computation is supported

Features selection

Given n-variate data, iteratively determines its k-marginals that are little informative. Uses C2- the covariance matrix, and CN - the Nth cumulant's tensor, both in the SymmetricTensor type, see SymmetricTensors.jl. Uses one of the following optimisation functions f: `["hosvd", "norm", "mev"].

julia> function cumfsel(C2::SymmetricTensor{T,2}, CN::SymmetricTensor{T, N}, f::String, k::Int = n) where {T <: AbstractFloat, N}

The "norm" uses the norm of the higher-order cumulant tensor, this is a benchmark method for comparison.

The "mev" uses only the corrlelation matrix, see: C. Sheffield, 'Selecting band combinations from multispectral data', Photogrammetric Engineering and Remote Sensing, vol. 51 (1985)

The "hosvd" uses the Higher Order Singular Value decomposition of cumulant's tensor to extract information. For the N=3 case, the Joint Skewness Band Selection (JSBS), see X. Geng, K. Sun, L. Ji, H. Tang & Y. Zhao 'Joint Skewness and Its Application in Unsupervised Band Selection for Small Target Detection Sci Rep. vol.5 (2015) (https://www.nature.com/articles/srep09915). For the JSBS application in biomedical data analysis see: M. Domino, K. Domino, Z. Gajewski, 'An application of higher order multivariate cumulants in modelling of myoelectrical activity of porcine uterus during early pregnancy', Biosystems (2018), (https://doi.org/10.1016/j.biosystems.2018.10.019). For N = 4 and N = 5 see also P. Głomb, K. Domino, M. Romaszewski, M. Cholewa 'Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images' (2018) (https://arxiv.org/abs/1808.03513).

julia> Random.seed!(42);

julia> using Cumulants

julia> using SymmetricTensors

julia> x = rand(12,10);

julia> c = cumulants(x, 4);

julia> cumfsel(c[2], c[4], "hosvd")
10-element Array{Any,1}:
 (Bool[true, true, true, false, true, true, true, true, true, true], 27.2519, 4)        
 (Bool[true, true, false, false, true, true, true, true, true, true], 22.6659, 3)       
 (Bool[true, true, false, false, false, true, true, true, true, true], 18.1387, 5)      
 (Bool[false, true, false, false, false, true, true, true, true, true], 14.4492, 1)     
 (Bool[false, true, false, false, false, true, true, false, true, true], 11.2086, 8)    
 (Bool[false, true, false, false, false, true, true, false, true, false], 7.84083, 10)  
 (Bool[false, false, false, false, false, true, true, false, true, false], 5.15192, 2)  
 (Bool[false, false, false, false, false, false, true, false, true, false], 2.56748, 6)
 (Bool[false, false, false, false, false, false, true, false, false, false], 0.30936, 9)
 (Bool[false, false, false, false, false, false, false, false, false, false], 0.0, 7)  

The output is the Array of tuples (ind::Array{Bool}, fval::Float64, i::Int), each tuple corresponds to the one step of the features selection. Marginals are removed in the information hierarchy, starting from the least informatve and ending on the most infomrative.

The vector ind consist of false that determines the removed marginal, and true that determines the left marginal.

The fval is the value of the target function.

The i numerates the marginal removed at the given step.

To limit number of steps use the default parameter k:

julia> cumfsel(Array(c[2]), Array(c[4]), "hosvd", 2)
2-element Array{Any,1}:
 (Bool[true, true, true, false, true, true, true, true, true, true], 27.2519, 4)
 (Bool[true, true, false, false, true, true, true, true, true, true], 22.6659, 3)

For the mev optimization run:

julia> cumfsel::SymmetricTensor{T,2}, k::Int = Σ.dats)

The higher-order cross-correlation matrix

  cum2mat(c::SymmetricTensor{T, N}) where {T <: AbstractFloat, N}

Returns the higher-order cross-correlation matrix in the form of SymmetricTensor{T, 2}. Such matrix is the contraction of the corresponding higher-order cumulant tensor c::SymmetricTensor{T, N} with itself in all modes but one.

julia> Random.seed!(42);

julia> t = rand(SymmetricTensor{Float64, 3}, 4);

julia> cum2mat(t)
SymmetricTensor{Float64,2}(Union{Nothing, Array{Float64,2}}[[7.69432 4.9757; 4.9757 5.72935] [6.09424 4.92375; 5.05157 3.17723]; nothing [7.33094 4.93128; 4.93128 4.7921]], 2, 2, 4, true)

Outliers detection

Let X be the multivariate data represented in the form of t x n matrix of Floats, t numerates the realisations, while n numerates the marginals.

RX detector

  rxdetect(X::Matrix{T}, α::Float64 = 0.99)

The RX (Reed-Xiaoli) Anomaly Detection returns the array of Bool, where true corresponds to the outlier realisations while false corresponds to the ordinary data. The parameter α is the sensitivity (threshold) parameter of the RX detector.

julia> Random.seed!(42);

julia> x = vcat(rand(8,2), 20*rand(2,2))
10×2 Array{Float64,2}:
  0.533183    0.956916
  0.454029    0.584284
  0.0176868   0.937466
  0.172933    0.160006
  0.958926    0.422956
  0.973566    0.602298
  0.30387     0.363458
  0.176909    0.383491
 11.8582      5.25618
 14.9036     10.059   

julia> rxdetect(x, 0.95)
10-element Array{Bool,1}:
 false
 false
 false
 false
 false
 false
 false
 false
  true
  true

The 4th order multivariate cumulant outlier detector

  function hosvdc4detect(X::Matrix{T}, β::Float64 = 4.1, r::Int = 3)

The 4th order multivariate cumulant outlier detector returns the array of Bool, where true corresponds to the outlier realisations while false corresponds to the ordinary data. The parameter β is the sensitivity parameter, the parameter r is the number of specific directions (with high 4th order cumulant) on which data are projected. See K. Domino: 'Multivariate cumulants in outlier detection for financial data analysis', [arXiv:1804.00541] (https://arxiv.org/abs/1804.00541).

julia> Random.seed!(42);

julia> x = vcat(rand(8,2), 20*rand(2,2))
10×2 Array{Float64,2}:
  0.533183    0.956916
  0.454029    0.584284
  0.0176868   0.937466
  0.172933    0.160006
  0.958926    0.422956
  0.973566    0.602298
  0.30387     0.363458
  0.176909    0.383491
 11.8582      5.25618
 14.9036     10.059

julia> rxdetect(x, 0.95)
10-element Array{Bool,1}:
 false
 false
 false
 false
 false
 false
 false
 false
  true
  true

Tests on artificial data.

In folder test\outliers_detect and test\features_select there are the Julia executable files for testing features selection and outliers detection on artificial data.

Features selection

In ./test/features_select the executable file gendat4selection.jl generates multivariate data where the subset of infomrative margianls is modelled by the t-Student copula with --nu degrees of freedom (by defalt 4). All univariate marginal distributions are t-Student with -nuu degrees of freedom (by defalt 25).

The gendat4selection.jl returns a .jld2 file with data. Run jkfs_selection.jl on this file to display the characteristics of features selection plotted in ./test/features_select/pics/

Outlier detection

In ./test/outliers_detect/ the executable file gendat4detection.jl generates multivariate data with outliers modelled by the t-Student copula with --nu degrees of freedom (by defalt 6). All univariate marginal distributions are t-Student with --nuu degrees of freedom (by defalt 6). The number of test realisations is --reals (by default 5).

The gendat4detection.jl returns a .jld2 file with data. Run detect_outliers.jl on this file to display the characteristics of outlier detection plotted in ./test/outliers_detect/pics/'

Citing this work

This project was partially financed by the National Science Centre, Poland – project number 2014/15/B/ST6/05204.

While using hosvdc4detect() please cite K. Domino: 'Multivariate cumulants in outlier detection for financial data analysis', [arXiv:1804.00541] (https://arxiv.org/abs/1804.00541).

While using cumfsel() please cite P. Głomb, K. Domino, M. Romaszewski, M. Cholewa 'Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images' (2018) (https://arxiv.org/abs/1808.03513).

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