Comparison of PCA, NNMF, and ICA over image de-noising


In a previous blog post, [1], I compared Principal Component Analysis (PCA) / Singular Value Decomposition (SVD) and Non-Negative Matrix Factorization (NNMF) over a collection of noised images of digit handwriting from the MNIST data set, [3], which is available in Mathematica.

This blog post adds to that comparison the use of Independent Component Analysis (ICA) proclaimed in my previous blog post, [1].


The ICA related additional computations to those in [1] follow.

First we load the package IndependentComponentAnalysis.m :


From that package we can use the function IndependentComponentAnalysis to find components:

{S, A} = IndependentComponentAnalysis[Transpose[noisyTestImagesMat], 9, PrecisionGoal -> 4.5];
Norm[noisyTestImagesMat - Transpose[S.A]]/Norm[noisyTestImagesMat]
(* 0.592739 *)

Let us visualize the norms of the mixing matrix A :

norms = Norm /@ A;
ListPlot[norms, PlotRange -> All, PlotLabel -> "Norms of A rows", 
    PlotTheme -> "Detailed"] // 
pos = OutlierPosition[norms, TopOutliers@*HampelIdentifierParameters]

Norms of the mixing matrix

Next we can visualize the found "source" images:

ncol = 2;
 MapIndexed[{#2[[1]], Norm[#], 
   ImageAdjust@Image[Partition[#, dims[[1]]]]} &, 
 Transpose[S]] /. (# -> Style[#, Red] & /@ pos),
Table["", {ncol - QuotientRemainder[Dimensions[S][[2]], ncol][[2]]}]
], ncol], Dividers -> All]

ICA found source images of 6 and 7 images matrix

After selecting several of source images we zero the rest by modifying the matrix A:

pos = {6, 7, 9};
norms = Norm /@ A;
dA = DiagonalMatrix[
 ReplacePart[ConstantArray[0, Length[norms]], Map[List, pos] -> 1]];
newMatICA = 
 Transpose[Map[# + Mean[Transpose[noisyTestImagesMat]] &, S.dA.A]];
  denoisedImagesICA = Map[Image[Partition[#, dims[[2]]]] &, newMatICA];

Visual comparison of de-noised images

Next we visualize the ICA de-noised images together with the original images and the SVD and NNMF de-noised ones.

There are several ways to present that combination of images.

Grayscale 6 and 7 images, orginal, noised, PCA, NNMF, ICA de-noised

Binarized 6 and 7 images, orginal, noised, PCA, NNMF, ICA de-noised

Image collage of orginal, noised, PCA, NNMF, ICA de-noised 6 and 7 images

Comparison using classifiers

We can further compare the de-noising results by building digit classifiers and running them over the de-noised images.

icaCM = ClassifierMeasurements[digitCF, 
    Thread[(Binarize[#, 0.55] &@*ImageAdjust@*ColorNegate /@ 
        denoisedImagesICA) -> testImageLabels]]

We can see that with ICA we get better results than with PCA/SVD, probably not as good as NNMF, but very close.

Classifier comparison over PCA, NNMF, ICA de-noised images of 6 and 7

All images of this blog post

Computations results for ICA application of noised handwriting images of 6 an 7


[1] A. Antonov, "Comparison of PCA and NNMF over image de-noising", (2016), MathematicaForPrediction at WordPress.

[2] A. Antonov, "Independent Component Analysis for multidimensional signals", (2016), MathematicaForPrediction at WordPress.

[3] Wikipedia entry, MNIST database.

Independent component analysis for multidimensional signals


Independent Component Analysis (ICA) is a (matrix factorization) method for separation of a multi-dimensional signal (represented with a matrix) into a weighted sum of sub-components that have less entropy than the original variables of the signal. See [1,2] for introduction to ICA and more details.

This blog post is to proclaim the implementation of the "FastICA" algorithm in the package IndependentComponentAnalysis.m and show a basic application with it. (I programmed that package last weekend. It has been in my ToDo list to start ICA algorithms implementations for several months… An interesting offshoot was the procedure I derived for the StackExchange question "Extracting signal from Gaussian noise".)

In this blog post ICA is going to be demonstrated with both generated data and "real life" weather data (temperatures of three cities within one month).

Generated data

In order to demonstrate ICA let us make up some data in the spirit of the "cocktail party problem".

(*Signal functions*)
Clear[s1, s2, s3]
s1[t_] := Sin[600 \[Pi] t/10000 + 6*Cos[120 \[Pi] t/10000]] + 1.2
s2[t_] := Sin[\[Pi] t/10] + 1.2
s3[t_?NumericQ] := (((QuotientRemainder[t, 23][[2]] - 11)/9)^5 + 2.8)/2 + 0.2

(*Mixing matrix*)
A = {{0.44, 0.2, 0.31}, {0.45, 0.8, 0.23}, {0.12, 0.32, 0.71}};

(*Signals matrix*)
nSize = 600;
S = Table[{s1[t], s2[t], s3[t]}, {t, 0, nSize, 0.5}];

(*Mixed signals matrix*)
M = A.Transpose[S];

   Plot[#, {t, 0, nSize}, PerformanceGoal -> "Quality", 
     ImageSize -> 250] &, {s1[t], s2[t], s3[t]}]}]

Original signals

(*Mixed signals*)
Grid[{Map[ListLinePlot[#, ImageSize -> 250] &, M]}]

Mixed signals

I took the data generation formulas from [6].

ICA application

Load the package:


It is important to note that the usual ICA model interpretation for the factorized matrix X is that each column is a variable (audio signal) and each row is an observation (recordings of the microphones at a given time). The matrix 3×1201 M was constructed with the interpretation that each row is a signal, hence we have to transpose M in order to apply the ICA algorithms, X=M^T.

X = Transpose[M];

{S, A} = IndependentComponentAnalysis[X, 3];

Check the approximation of the obtained factorization:

Norm[X - S.A]    
(* 3.10715*10^-14 *)

Plot the found source signals:

Grid[{Map[ListLinePlot[#, PlotRange -> All, ImageSize -> 250] &, 

Found source signals

Because of the random initialization of the inverting matrix in the algorithm the result my vary. Here is the plot from another run:

Found source signals 2

The package also provides the function FastICA that returns an association with elements that correspond to the result of the function fastICA provided by the R package "fastICA". See [4].

Here is an example usage:

res = FastICA[X, 3];

(* {"X", "K", "W", "A", "S"} *)

   ListLinePlot[#, PlotRange -> All, ImageSize -> Medium] &, 

FastICA found source signals

Note that (in adherence to [4]) the function FastICA returns the matrices S and A for the centralized matrix X. This means, for example, that in order to check the approximation proper mean has to be supplied:

Norm[X - Map[# + Mean[X] &, res["S"].res["A"]]]
(* 2.56719*10^-14 *)

Signatures and results

The result of the function IndependentComponentAnalysis is a list of two matrices. The result of FastICA is an association of the matrices obtained by ICA. The function IndependentComponentAnalysis takes a method option and options for precision goal and maximum number of steps:

In[657]:= Options[IndependentComponentAnalysis]

Out[657]= {Method -> "FastICA", MaxSteps -> 200, PrecisionGoal -> 6}

The intent is IndependentComponentAnalysis to be the front interface to different ICA algorithms. (Hence, it has a Method option.) The function FastICA takes as options the named arguments of the R function fastICA described in [4].

In[658]:= Options[FastICA]

Out[658]= {"NonGaussianityFunction" -> Automatic, 
 "NegEntropyFactor" -> 1, "InitialUnmixingMartix" -> Automatic, 
 "RowNorm" -> False, MaxSteps -> 200, PrecisionGoal -> 6, 
 "RFastICAResult" -> True}

At this point FastICA has only the deflation algorithm described in [1]. ([4] has also the so called "symmetric" ICA sub-algorithm.) The R function fastICA in [4] can use only two neg-Entropy functions log(cosh(x)) and exp(-u^2/2). Because of the symbolic capabilities of Mathematica FastICA of [3] can take any listable function through the option "NonGaussianityFunction", and it will find and use the corresponding first and second derivatives.

Using NNMF for ICA

It seems that in some cases, like the generated data used in this blog post, Non-Negative Matrix Factorization (NNMF) can be applied for doing ICA.

To be clear, NNMF does dimension reduction, but its norm minimization process does not enforce variable independence. (It enforces non-negativity.) There are at least several articles discussing modification of NNMF to do ICA. For example [6].

Load NNMF package [5] (from MathematicaForPrediction at GitHub):


After several applications of NNMF we get signals close to the originals:

{W, H} = GDCLS[M, 3];
Grid[{Map[ListLinePlot[#, ImageSize -> 250] &, Normal[H]]}]

NNMF found source signals

For the generated data in this blog post, FactICA is much faster than NNMF and produces better separation of the signals with every run. The data though is a typical representative for the problems ICA is made for. Another comparison with image de-noising, extending my previous blog post, will be published shortly.

ICA for mixed time series of city temperatures

Using Mathematica‘s function WeatherData we can get temperature time series for a small set of cities over a certain time grid. We can mix those time series into a multi-dimensional signal, MS, apply ICA to MS, and judge the extracted source signals with the original ones.

This is done with the following commands.

Get time series data

cities = {"Sofia", "London", "Copenhagen"};
timeInterval = {{2016, 1, 1}, {2016, 1, 31}};
ts = WeatherData[#, "Temperature", timeInterval] & /@ cities;

opts = {PlotTheme -> "Detailed", FrameLabel -> {None, "temperature,\[Degree]C"}, ImageSize -> 350};
    PlotLabel -> "City temperatures\nfrom " <> DateString[timeInterval[[1]], {"Year", ".", "Month", ".", "Day"}] <> 
    " to " <> DateString[timeInterval[[2]], {"Year", ".", "Month", ".", "Day"}], 
    PlotLegends -> cities, ImageSize -> Large, opts]

City temperatures

Cleaning and resampling (if any)

Here we check the data for missing data:

Length /@ Through[ts["Path"]]
Count[#, _Missing, \[Infinity]] & /@ Through[ts["Path"]]
(* {1483, 1465, 742} *)
(* {0,0,0} *)
(* 0 *)

Resampling per hour:

ts = TimeSeriesResample[#, "Hour", ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}] & /@ ts

Mixing the time series

In order to do a good mixing we select a mixing matrix for which all column sums are close to one:

mixingMat = #/Total[#] & /@ RandomReal[1, {3, 3}];
(* mixingMat = {{0.357412, 0.403913, 0.238675}, {0.361481, 0.223506, 0.415013}, {0.36564, 0.278565, 0.355795}} *)
(* {1.08453, 0.905984, 1.00948} *)

Note the row normalization.

Make the mixed signals:

tsMixed = Table[TimeSeriesThread[mixingMat[[i]].# &, ts], {i, 3}]

Plot the original and mixed signals:

Grid[{{DateListPlot[ts, PlotLegends -> cities, PlotLabel -> "Original signals", opts],
DateListPlot[tsMixed, PlotLegends -> Automatic, PlotLabel -> "Mixed signals", opts]}}]

Original and mixed temperature signals

Application of ICA

At this point we apply ICA (probably more than once, but not too many times) and plot the found source signals:

X = Transpose[Through[tsMixed["Path"]][[All, All, 2]] /. Quantity[v_, _] :> v];
{S, A} = IndependentComponentAnalysis[X, 3];
DateListPlot[Transpose[{tsMixed[[1]]["Dates"], #}], PlotTheme -> "Detailed", ImageSize -> 250] & /@ Transpose[S]

ICA found temperature time series components

Compare with the original time series:

MapThread[DateListPlot[#1, PlotTheme -> "Detailed", PlotLabel -> #2, ImageSize -> 250] &, {tsPaths, cities}]

Original temperature time series

After permuting and inverting some of the found sources signals we see they are fairly good:

pinds = {3, 1, 2};
pmat = IdentityMatrix[3][[All, pinds]];

DateListPlot[Transpose[{tsMixed[[1]]["Dates"], #}], PlotTheme -> "Detailed", ImageSize -> 250] & /@ 
  Transpose[S.DiagonalMatrix[{1, -1, 1}].pmat]

Permuted and inverted found source signals


[1] A. Hyvarinen and E. Oja (2000) Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430 . URL: .

[2] Wikipedia entry, Independent component analysis, URL: .

[3] A. Antonov, Independent Component Analysis Mathematica package, (2016), source code MathematicaForPrediction at GitHub, package IndependentComponentAnalysis.m .

[4] J. L. Marchini, C. Heaton and B. D. Ripley, fastICA, R package, URLs:, .

[5] A. Antonov, Implementation of the Non-Negative Matrix Factorization algorithm in Mathematica, (2013), source code at MathematicaForPrediction at GitHub, package NonNegativeMatrixFactorization.m.

[6] H. Hsieh and J. Chien, A new nonnegative matrix factorization for independent component analysis, (2010), Conference: Acoustics Speech and Signal Processing (ICASSP).

Comparison of PCA and NNMF over image de-noising

This post compares two dimension reduction techniques Principal Component Analysis (PCA) / Singular Value Decomposition (SVD) and Non-Negative Matrix Factorization (NNMF) over a set of images with two different classes of signals (two digits below) produced by different generators and overlaid with different types of noise.

PCA/SVD produces somewhat good results, but NNMF often provides great results. The factors of NNMF allow interpretation of the basis vectors of the dimension reduction, PCA in general does not. This can be seen in the example below.


First let us get some images. I am going to use the MNIST dataset for clarity. (And because I experimented with similar data some time ago — see Classification of handwritten digits.).

MNISTdigits = ExampleData[{"MachineLearning", "MNIST"}, "TestData"];
{testImages, testImageLabels} = 
  Transpose[List @@@ RandomSample[Cases[MNISTdigits, HoldPattern[(im_ -> 0 | 4)]], 100]];

enter image description here

See the breakdown of signal classes:

(* {{4, 48}, {0, 52}} *)

Verify the images have the same sizes:

Tally[ImageDimensions /@ testImages]
dims = %[[1, 1]]

Add different kinds of noise to the images:

noisyTestImages6 = 
    {RandomChoice[{"GaussianNoise", "PoissonNoise", "SaltPepperNoise"}], RandomReal[1]}], {i, Length[testImages]}];
RandomSample[Thread[{testImages, noisyTestImages6}], 15]

enter image description here

Since the important values of the signals are 0 or close to 0 we negate the noisy images:

negNoisyTestImages6 = ImageAdjust@*ColorNegate /@ noisyTestImages6

enter image description here

Linear vector space representation

We unfold the images into vectors and stack them into a matrix:

noisyTestImagesMat = (Flatten@*ImageData) /@ negNoisyTestImages6;

(* {100, 784} *)

Here is centralized version of the matrix to be used with PCA/SVD:

cNoisyTestImagesMat = 
  Map[# - Mean[noisyTestImagesMat] &, noisyTestImagesMat];

(With NNMF we want to use the non-centralized one.)

Here confirm the values in those matrices:

Grid[{Histogram[Flatten[#], 40, PlotRange -> All, 
     ImageSize -> Medium] & /@ {noisyTestImagesMat, 

enter image description here

SVD dimension reduction

For more details see the previous answers.

{U, S, V} = SingularValueDecomposition[cNoisyTestImagesMat, 100];
ListPlot[Diagonal[S], PlotRange -> All, PlotTheme -> "Detailed"]
dS = S;
Do[dS[[i, i]] = 0, {i, Range[10, Length[S], 1]}]
newMat = U.dS.Transpose[V];
denoisedImages = 
  Map[Image[Partition[# + Mean[noisyTestImagesMat], dims[[2]]]] &, newMat];

enter image description here

Here are how the new basis vectors look like:

Take[#, 50] &@
 MapThread[{#1, Norm[#2], 
    ImageAdjust@Image[Partition[Rescale[#3], dims[[1]]]]} &, {Range[
    Dimensions[V][[2]]], Diagonal[S], Transpose[V]}]

enter image description here

Basically, we cannot tell much from these SVD basis vectors images.

Load packages

Here we load the packages for what is computed next:



The blog post “Statistical thesaurus from NPR podcasts” discusses an example application of NNMF and has links to documents explaining the theory behind the NNMF utilization.

The outlier detection package is described in a previous blog post “Outlier detection in a list of numbers”.


This command factorizes the image matrix into the product W H :

{W, H} = GDCLS[noisyTestImagesMat, 20, "MaxSteps" -> 200];
{W, H} = LeftNormalizeMatrixProduct[W, H];


(* {100, 20} *)
(* {20, 784} *)

The rows of H are interpreted as new basis vectors and the rows of W are the coordinates of the images in that new basis. Some appropriate normalization was also done for that interpretation. Note that we are using the non-normalized image matrix.

Let us see the norms of $H$ and mark the top outliers:

norms = Norm /@ H;
ListPlot[norms, PlotRange -> All, PlotLabel -> "Norms of H rows", 
  PlotTheme -> "Detailed"] // 
OutlierPosition[norms, TopOutliers@*HampelIdentifierParameters]
OutlierPosition[norms, TopOutliers@*SPLUSQuartileIdentifierParameters]

enter image description here

Here is the interpretation of the new basis vectors (the outliers are marked in red):

MapIndexed[{#2[[1]], Norm[#], Image[Partition[#, dims[[1]]]]} &, H] /. (# -> Style[#, Red] & /@ 
   OutlierPosition[norms, TopOutliers@*HampelIdentifierParameters])

enter image description here

Using only the outliers of $H$ let us reconstruct the image matrix and the de-noised images:

pos = {1, 6, 10}
dHN = Total[norms]/Total[norms[[pos]]]*
    ReplacePart[ConstantArray[0, Length[norms]], 
     Map[List, pos] -> 1]];
newMatNNMF = W.dHN.H;
denoisedImagesNNMF = 
  Map[Image[Partition[#, dims[[2]]]] &, newMatNNMF];

Often we cannot just rely on outlier detection and have to hand pick the basis for reconstruction. (Especially when we have more than one classes of signals.)


At this point we can plot all images together for comparison:

imgRows = 
  Transpose[{testImages, noisyTestImages6, 
    ImageAdjust@*ColorNegate /@ denoisedImages, 
    ImageAdjust@*ColorNegate /@ denoisedImagesNNMF}];
With[{ncol = 5}, 
 Grid[Prepend[Partition[imgRows, ncol], 
   Style[#, Blue, FontFamily -> "Times"] & /@ 
    Table[{"original", "noised", "SVD", "NNMF"}, ncol]]]]

enter image description here

We can see that NNMF produces cleaner images. This can be also observed/confirmed using threshold binarization — the NNMF images are much cleaner.

imgRows =
  With[{th = 0.5},
   MapThread[{#1, #2, Binarize[#3, th], 
      Binarize[#4, th]} &, {testImageLabels, noisyTestImages6, 
     ImageAdjust@*ColorNegate /@ denoisedImages, 
     ImageAdjust@*ColorNegate /@ denoisedImagesNNMF}]];
With[{ncol = 5}, 
 Grid[Prepend[Partition[imgRows, ncol], 
   Style[#, Blue, FontFamily -> "Times"] & /@ 
    Table[{"label", "noised", "SVD", "NNMF"}, ncol]]]]

enter image description here

Usually with NNMF in order to get good results we have to do more that one iteration of the whole factorization and reconstruction process. And of course NNMF is much slower. Nevertheless, we can see clear advantages of NNMF’s interpretability and leveraging it.

Gallery with other experiments

In those experiments I had to hand pick the NNMF basis used for the reconstruction. Using outlier detection without supervision would not produce good results most of the time.

Further comparison with Classify

We can further compare the de-noising results by building signal (digit) classifiers and running them over the de-noised images.

For such a classifier we have to decide:

  1. do we train only with images of the two signal classes or over a larger set of signals;
  2. how many signals we train with;
  3. with what method the classifiers are built.

Below I use the default method of Classify with all digit images in MNIST that are not in the noised images set. The classifier is run with the de-noising traced these 0-4 images:

Get the images:

{trainImages, trainImageLabels} = Transpose[List @@@ Cases[MNISTdigits, HoldPattern[(im_ -> _)]]];
pred = Map[! MemberQ[testImages, #] &, trainImages];
trainImages = Pick[trainImages, pred];
trainImageLabels = Pick[trainImageLabels, pred];
(* {{0, 934}, {1, 1135}, {2, 1032}, {3, 1010}, {4, 928}, {5, 892}, {6, 958}, {7, 1028}, {8, 974}, {9, 1009}} *)

Build the classifier:

digitCF = Classify[trainImages -> trainImageLabels]

Compute classifier measurements for the original, the PCA de-noised, and NNMF de-noised images:

origCM = ClassifierMeasurements[digitCF, Thread[(testImages) -> testImageLabels]]
pcaCM = ClassifierMeasurements[digitCF, 
      Thread[(Binarize[#, 0.55] &@*ImageAdjust@*ColorNegate /@ denoisedImages) -> testImageLabels]]
nnmfCM = ClassifierMeasurements[digitCF, 
      Thread[(Binarize[#, 0.55] &@*ImageAdjust@*ColorNegate /@  denoisedImagesNNMF) -> testImageLabels]]

Tabulate the classification measurements results:

Grid[{{"Original", "PCA", "NNMF"},
{Row[{"Accuracy:", origCM["Accuracy"]}], Row[{"Accuracy:", pcaCM["Accuracy"]}], Row[{"Accuracy:", nnmfCM["Accuracy"]}]},
{origCM["ConfusionMatrixPlot"], pcaCM["ConfusionMatrixPlot"], nnmfCM["ConfusionMatrixPlot"]}}, 
Dividers -> {None, {True, True, False}}]


Finding outliers in 2D and 3D numerical data


This blog post describes a method of finding outliers in 2D and 3D data using Quantile Regression Envelopes discussed in previous blog posts: “Directional quantile envelopes”, “Directional quantile envelopes in 3D”.


In order to provide a good example of the method application it would be better to use “real life” data. I found this one: “UCI Online Retail Data Set”. That dataset has columns for online purchase transactions (quantity and price).

I had problems loading the data with this command:


so I downloaded the XLSX file and saved it into a CSV file, then imported it:

data = Import["~/Datasets/UCI Online Retail Data Set/Online Retail.csv", 
   "IgnoreEmptyLines" -> True, "HeaderLines" -> 0];
columnNames = data[[1]];
data = Rest[data];

Here are the dimensions of the dataset (seems “realistically” large):

(* {65499, 8} *)

Here is a summary of the quantative columns:

Grid[{RecordsSummary[N@data[[All, {4, 6}]], columnNames[[{4, 6}]]]}, Dividers -> All]

enter image description here

Adding a “discount” column

Since we have only two quantative columns in the data let us add a third one, and make it have 3 outliers.

dvec = RandomReal[SkewNormalDistribution[1, 2, 4], Dimensions[data][[1]]];
Block[{inds = RandomSample[Range[Length[dvec]], 3]}, 
  dvec[[inds]] = 10*dvec[[inds]]];
testData = MapThread[Append, {N@data[[All, {4, 6}]], dvec}];

Here is the summary:

Grid[{RecordsSummary[testData]}, Dividers -> All]

enter image description here


It is a good idea to standardize the data. This is not necessary for the outlier finding procedure used below, but it makes the data more convenient for visualization or other exploration.

sTestData = Transpose[Standardize /@ Transpose[N@testData]];

Because of the outliers plotting the data might produce uninformative plots. We can use logarithms of the point coordinates and for that we have to shift the standardized data to be positive. This is done with this command:

Block[{offset = -2 (Min /@ Transpose[sTestData])}, 
  sTestData = Map[# + offset &, sTestData]];

Let use show the standardized data summary and visualize:

Grid[{RecordsSummary[sTestData]}, Dividers -> All]

enter image description here

opts = {PlotRange -> All, ImageSize -> Medium, 
  PlotTheme -> "Detailed"}; Grid[{{ListPointPlot3D[sTestData, opts], 
   ListPointPlot3D[Log10@sTestData, opts]}}]

enter image description here

Using Quantile Regression envelopes to find outliers

We are going to find the outliers by computing envelopes around the dataset points that contain almost all points (e.g. 99.7% of them).

The finding of directional quantile envelopes in 2D and 3D is explained in these blog posts:

  1. “Directional quantile envelopes”,

  2. “Directional quantile envelopes in 3D”.

I am a big fan of Quantile Regression (QR) and I have implemented a collection of functions and applications of QR. See these blog posts.

This command imports the package QuantileRegression.m :


Here is an example of an envelope found using directional quantiles over a small sample of the points:

Block[{testData = RandomSample[sTestData, 2000], qreg},
 qreg = QuantileEnvelopeRegion[testData, 0.997, 10];
 Show[{Graphics3D[{Red, Point[testData]}, Axes -> True], 

enter image description here

The outlier points (in red) are outside of the envelope.

The example (and plot) above are just for illustration purposes. We calculate a quantile evelope region using all points.

Block[{testData = sTestData},
 qreg = QuantileEnvelopeRegion[testData, 0.9997, 10];

(The function QuantileEnvelopeRegion works also for 2D. For 2D nothing else changes in the steps below except using 2D graphics.)

This command makes a function to test does a point belong to the found envelope region or not:

rmFunc = RegionMember[qreg];

This calculates the membership predicates for all points:

 pred = rmFunc /@ sTestData;

(* {15.6485, Null} *)

And we can see the membership breakdown:

(* {{True, 65402}, {False, 97}} *)

and visualize it (using Pick and taking logarithms):

Graphics3D[{Gray, Point[Log10@Pick[sTestData, pred]], Red, 
  Point[Log10@Pick[sTestData, Not /@ pred]]}, Axes -> True]

enter image description here

The plot above contains both top and bottom outliers (in red). If we are intereseted only in the top outliers we can find these thresholds:

topThresholds = Quantile[#, 0.95] & /@ Transpose[testData]
(* {25, 10.95, 4.89433} *)

and use them to select the top outliers:

Select[Pick[testData, Not /@ pred], 
 Total[Thread[# > topThresholds] /. {True -> 1, False -> 0}] > 1 &]

(* {{1, 836.14, 6.48564}, {1, 16.13, 8.71455}, {5, 25.49, 8.47351}, {-1, 1126, 5.25211}, {1000, 0, 5.4212}, {1, 15.79, 9.14674}, {-1, 544.4, 6.86266}} *)

Note that the quantile regression envelope and the membership predicates were computed over the standardized data, and the predicates were used to retrieve the outliers of the original data.