Univ. - URJC BB… · • Data sketching (subsampling) • Big data tensors (parallel factor...
Transcript of Univ. - URJC BB… · • Data sketching (subsampling) • Big data tensors (parallel factor...
Addisson Salazar, Univ. Politècnica de València 1
Instituto Telecomunicaciones y Aplicaciones Multimedia
Procesado de señal y fusión de clasificadores: detección de fraude y
otras aplicaciones
1
Universitat Politècnica de València
24‐06‐2016
Dr. Addisson Salazar
Instituto Telecomunicaciones y Aplicaciones Multimedia
ContentsContents
Background of the GTS
• Non‐Destructive Testing
• Surveillance Systems
• Biomedical Analysis
• Financial Analysis
Pattern Recognition Approach
Background of the GTS
• Non‐Destructive Testing
• Surveillance Systems
• Biomedical Analysis
• Financial Analysis
Pattern Recognition Approach
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• Statement of the problem • Available platforms: in‐Fusion, Neurodyn• Application General Outline
Recent Themes in Signal Processing
Examples of Applications
• Credit card fraud detection
• Microarousal detection, neuropsychological tests
• Statement of the problem • Available platforms: in‐Fusion, Neurodyn• Application General Outline
Recent Themes in Signal Processing
Examples of Applications
• Credit card fraud detection
• Microarousal detection, neuropsychological tests
Instituto Telecomunicaciones y Aplicaciones Multimedia
ContentsContents
Background of the GTSBackground of the GTS
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GTS BackgroundGTS Background
Applications: Material quality control, Biomedical diagnosis, Bank card fraud, Surveillance, Image processing, …
R h bj t St ti ti l i l i N
Applications: Material quality control, Biomedical diagnosis, Bank card fraud, Surveillance, Image processing, …
R h bj t St ti ti l i l i N
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Research subjects: Statistical signal processing, Non‐Gaussian mixtures, Non‐linear processing, Dynamic modeling, Decision fusion, Machine learning, Signal processing on Graphs
Research subjects: Statistical signal processing, Non‐Gaussian mixtures, Non‐linear processing, Dynamic modeling, Decision fusion, Machine learning, Signal processing on Graphs
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GTS ‐ Non Destructive TestingGTS ‐ Non Destructive Testing
Quality control of marble rocks (US, I‐E) Quality control of marble rocks (US, I‐E)
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Chronological classification of archaeological ceramics (US) Chronological classification of archaeological ceramics (US)
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GTS ‐ Non Destructive TestingGTS ‐ Non Destructive Testing
Material consolidation and thickness layer detection
(US)
Material consolidation and thickness layer detection
(US)
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(US)(US)
Foreign body detection in food
(US)
Foreign body detection in food
(US)
Flaw detection and material characterization in historical walls
(US, I‐E, GPR)
Flaw detection and material characterization in historical walls
(US, I‐E, GPR)
Addisson Salazar, Univ. Politècnica de València 2
Instituto Telecomunicaciones y Aplicaciones Multimedia
GTS ‐ Surveillance SystemsGTS ‐ Surveillance Systems
Multimodal surveillance Multimodal surveillance Audio
Visible video Fusion?
Infrared video
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Early forest fire detection Early forest fire detection Intrusion detection Intrusion detection
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GTS – Biomedical analysisGTS – Biomedical analysis
Apnea diagnosis
(EEG, EMG,
EOG)
Apnea diagnosis
(EEG, EMG,
EOG)
50 100 150 200 250 300 350 4001
1.5
2
50 100 150 200 250 300 350 4001
1.5
2
50 100 150 200 250 300 350 4001
1.5
2
SICAMM
ICAMM
Expert
x 104
Epoch number
8
Atrialfibrillation
(ECG)
Atrialfibrillation
(ECG)
0 0.5 1 1.5 2
x 107
-5
0
5
x 10
sin
us
rhyt
hm
time
0 0.5 1 1.5 2
x 107
-5
0
5x 10
4
atr
ial f
ibri
llatio
n
time
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GTS – Biomedical analysisGTS – Biomedical analysis
Cognitive structures, Epilepsy, Alzheimer
(EEG, ECoG,
fMRI, DTI)
Cognitive structures, Epilepsy, Alzheimer
(EEG, ECoG,
fMRI, DTI)
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GTS – Biomedical analysisGTS – Biomedical analysis
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GTS ‐WebminingGTS ‐Webmining
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Processing
OrganizationUnderstanding
+ Sequential
+ Global
+ Active + Reflective
+ Inductive
+ Deductive
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2
3
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GTS ‐WebminingGTS ‐Webmining
Agenda
Forum Workgroupdocuments
?? ?
Exercises Contents
Chat
Access
51 4
INTERACTIV
ITY
AL ACTIV
ITY
2
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? ?AchievementNews
3 PERSONAL
Addisson Salazar, Univ. Politècnica de València 3
Instituto Telecomunicaciones y Aplicaciones Multimedia
GTS ‐ Credit Card Fraud AnalysisGTS ‐ Credit Card Fraud Analysis
Fraud detection in bank cards operations
Fraud detection in bank cards operations
Operationd
?
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¿Fraud?
record
R=
commerce
city
codes
identifyiers
amount
method
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0
0.5
1
0
0.5
1
0
10
20
30
KL
dis
tanc
e
Frauddetection
OLAP
2
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GTS ‐ Credit Card Fraud AnalysisGTS ‐ Credit Card Fraud Analysis
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11outliers
supervision
Modelestimation
1
Models Results
0 0.02 0.04 0.06 0.08 0.10.1
0.2
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False Positive Rate
Tru
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osit
ive
Rat
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ContentsContents
Pattern Recognition ApproachPattern Recognition Approach
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g ppg pp
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Problem statement from Pattern Recognition
Problem statement from Pattern Recognition
FeatureextractionFeatureextraction
ClassificationClassification ScoreScore
ApplicationApplication??
ss µµ
One method solving all
M l i l h d
One method solving all
M l i l h d
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domaindomain ??
Knowledge about each category
Extreme case:
• Much and diverse information about a category
• A few information about the other category
Knowledge about each category
Extreme case:
• Much and diverse information about a category
• A few information about the other category
Sources
‐ Physical models
‐ Databases
Sources
‐ Physical models
‐ Databases
Multiple methods (collaborative working) Multiple methods
(collaborative working)
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Architecture based onMultiple Classifiers
Architecture based onMultiple Classifiers
General classifiers
Specialized classifiers in different feature space zones (Mixture of
experts)
Multiple classifiers performing in sequence
Specialized classifiers in each of the feature vector components
Multiple classifiers performing in different space‐time coordinates
General classifiers
Specialized classifiers in different feature space zones (Mixture of
experts)
Multiple classifiers performing in sequence
Specialized classifiers in each of the feature vector components
Multiple classifiers performing in different space‐time coordinates
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Multiple classifiers performing in different space time coordinates
Pool of competitive and collaborative weak classifiers (Boosting)
Multiple classifiers performing in different space time coordinates
Pool of competitive and collaborative weak classifiers (Boosting)
Schemes of training:
• Un‐supervised, semi‐supervised
• Different historical dataset versions
• Different localization dataset versions
Schemes of training:
• Un‐supervised, semi‐supervised
• Different historical dataset versions
• Different localization dataset versions
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Available platforms: in‐FusionAvailable platforms: in‐Fusion
FeatureextractionFeatureextraction
channel 1channel 1
channel nchannel n
...
...
filteringfiltering timetime frequencyfrequency statisticsstatistics
Pre‐processing
Pre‐processing
EarlyFusionEarlyFusion
cleaningcleaning augmentationaugmentation
indirectfeaturesindirectfeatures
splittingsplitting
rankingranking
functionfunction
dimensionreductiondimensionreduction
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FusionFusion
Training /Testing ‐ 1Training /Testing ‐ 1
LateFusionLateFusion
Training /Testing ‐ nTraining /Testing ‐ n
......
FinalRepresentation
FinalRepresentation
Knowledge
Datamodeling
PDFestimation
Knowledge
Datamodeling
PDFestimation
PriorsPriors
GMMGMM ICAMMICAMM
parametric / non‐parametric
parametric / non‐parametric
Addisson Salazar, Univ. Politècnica de València 4
Instituto Telecomunicaciones y Aplicaciones Multimedia
Available platforms: NeurodynAvailable platforms: Neurodynchannel 1channel 1
channel nchannel n
...
...
FeatureextractionFeatureextraction
Pre‐processing
Pre‐processing
ss ss
EarlyFusionEarlyFusion
tt ,,ss
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FinalRepresentation
FinalRepresentation
......Training /Testing ‐ 1Training /Testing ‐ 1
Training /Testing ‐ nTraining /Testing ‐ n
tt ,,ss
tt ,,ss
LateFusionLateFusion
tt ,,ss
UGSICAMMUGSICAMMSICAMMSICAMM
parametric / non‐parametric
parametric / non‐parametricPriorsPriors
temporal / Spatial codingtemporal /
Spatial coding
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Application General OutlineApplication General Outline
in‐Fusionin‐Fusion
NeurodynNeurodyn
Newdevelopments
Newdevelopments
Adaptation &DevelopmentAdaptation &Development
PrototypePrototype
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developmentsdevelopments
Objectives• To improve the detection capabilities of the system in use• To improve the predictive capabilities of the system in use• To provide results from single and fused methods• To provide several levels of spatial and temporal coding• To accomplish the required standards
Objectives• To improve the detection capabilities of the system in use• To improve the predictive capabilities of the system in use• To provide results from single and fused methods• To provide several levels of spatial and temporal coding• To accomplish the required standards
Instituto Telecomunicaciones y Aplicaciones Multimedia
ContentsContents
Recent Themes in Signal ProcessingRecent Themes in Signal Processing
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g gg g
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ChallengesModels and optimization
Signal processing and learning for Big Data
Real time
Outliers, missing values
Massive scaleParallel,
Descentralized
Time/data adaptive
Recent Themes in Signal ProcessingRecent Themes in Signal Processing
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Tasks
Prediction, forecasting
Cleansing, imputation
Regression, classification, clustering
Dimensionality reduction
Cloud storage
Real‐time constraints
Succint, sparse
Robust
Instituto Telecomunicaciones y Aplicaciones Multimedia
denotes a low rank matrixxN TL xM TS sparse matrix
xN TY large‐scale data set can be defined as
Y L DS V
xN TV for modeling and measurement errors
xN MD dictionary
given
Possible DefinitionsPossible Definitions
Y L DS V
1, x 1,N T no nulls index pairs
P P Y L DS V
ExampleNetwork anomaly detection: Y is traffic volume over N links and T slots;L is the nominal link‐level traffic; D is link x flow binary routing matrix;S is parse anomalous flow
,n t
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Feature Description
DataRepresentation Feedback
Feature Life CycleFeature Life Cycle
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Feature Selection Feature Evaluation
Data Collection
FeedbackLearning
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Instituto Telecomunicaciones y Aplicaciones Multimedia
• Decentralized and parallel algorithms
• Splitting, sequential algorithms
• Online algorithms for streaming analytics
Large‐scale problems‐‐‐>
Algorithms
Algorithms and DataAlgorithms and Data
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Low‐complexity, real‐time algorithms capable of processing massive data sets in a parallelizable and/or fully decentralized fashion
• Data sketching (subsampling)
• Big data tensors (parallel factor analysis)
• Non‐linear modeling via kernel functions (tensor completion problem)
Data
1r r r
r
a b c
Y
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Signal Processing on GraphsSignal Processing on Graphs
, 1...nv n N Graph: set of connected nodes
, nmn m aA Adjacency matrix
, 1...ns n N Signal on graph (each node is assigned certain number )nv ns
Periodic signal
Activation signal of brain centers
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Signal on graph in semisupervisedscenario (5 of 8 nodes are of unknown class)
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Multi‐Classifier DecisionMulti‐Classifier Decision
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ContentsContents
Examples of ApplicationsExamples of Applications
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p ppp pp
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Fraud detection – General Outline
...CLASIF
1CLASIF
1DETECTOR
1
CLASIF1
CLASIF1
DETECTORN
x
P1 PN
P
Pn= Pn[H1/x]
1-Pn= Pn[H0/x]
FUSION
>< u
H1
H0
(0,1)
ƒ(P/H1)
ƒ(P/H1) + ƒ(P/H0)P= P[H1/P] =
P
∫u
∞
ƒ(P/H0)dp = PFA
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Fraud detection – Procedure Stages
Transactions
ConfirmedFrauds
Recordcrossing
Labelledtransactions
Dimensionalityreduction
Preprocessedtransactions
Direct featureextraction
Indirect featureextraction
Preprocessedtransactions
Recordselection
Trainingtransactions
Prototype fraudselection
Trainingtransactions
Testingtransactions
Fraud replicategeneration
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Trainingclassifier 1
TestClassifier 1
Classifier -1 scores
Trainingclassifier 2
PC 1
Entry codeselection
PC 2Test
Classifier 2
Testingtransactions
1
Fraud detection – Procedure Stages
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Trainingtransactions
classifier 2
Trainingclassifier 3
PC 3
Classifier 2
TestClassifier 3
Classifier -2 scores
Classifier -3 scores
Fusion
Fusionscores
Resultcalculation
1
Result tablesAnalysis graphs
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KPI Definition
VDRValue Detection Rate.The total fraud percentage saved by the system for a certain cutoff values of score
ADRAccount Detection Rate.The percentage of detected cards
ADTAverage Detected Transaction. The mean amount of transactions required for detecting a fraudulent card
AFPR Account False to Positive Rate
Key Performance Indicators
True positives + False positives False positivesAFPR= =1+
True positives True positives
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Minimum Mean
score
>=
ADR
(%)
VDR
(%) ADT
FPR
(%) score
>=
ADR
(%)
VDR
(%) ADT
FPR
(%)
0 100.00 100.00 1.0 100 0 100.00 100.00 1.0 100
5 56.74 49.90 1.6 4 5 97.36 99.58 1.0 69
10 54.84 48.39 1.7 4 10 95.75 99.47 1.0 59
15 51.47 47.40 1.7 3 15 93.70 99.17 1.1 46
20 48.53 46.63 1.8 3 20 92.96 99.04 1.1 35
25 46.48 46.12 1.8 2 25 91.06 98.91 1.1 26
30 45.01 44.65 1.8 2 30 88.42 98.05 1.1 20
35 43.26 44.18 1.9 2 35 84.60 92.26 1.1 14
Example of normalized KPI tables
35 43.26 44.18 1.9 35 84.60 92.26 1.1 4
40 40.62 43.44 2.0 1 40 80.65 90.39 1.2 13
45 34.90 41.45 2.2 1 45 76.69 89.25 1.2 11
50 33.58 40.75 2.1 1 50 72.29 87.88 1.2 9
55 32.11 38.85 2.2 1 55 67.16 85.67 1.2 7
60 26.69 37.67 2.4 .8 60 61.58 80.90 1.3 6
65 23.02 35.03 2.4 .7 65 47.80 73.65 1.3 3
70 21.41 32.53 2.6 .6 70 31.52 39.84 2.2 1
75 20.38 31.82 2.2 .6 75 24.78 37.80 2.3 .8
80 17.74 30.58 2.3 .5 80 22.43 34.34 2.5 .6
85 15.84 27.06 2.6 .4 85 18.91 32.31 2.2 .5
90 14.08 25.50 2.5 .3 90 15.84 29.49 2.5 .4
95 10.70 22.22 2.8 .2 95 13.64 25.32 2.7 .2
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0.6
0.8
1
siti
ve R
ate
LDA
0.5
0.6
0.7
0.8
siti
ve R
ate
ROC Curves for a Given Dataset
0 0.2 0.4 0.6 0.8 10
0.2
0.4
False Positive Rate
Tru
e P
o LDAQDANGMFusion-MEANFusion-MEDIANFusion-MIN
0 0.02 0.04 0.06 0.08 0.10.1
0.2
0.3
0.4
False Positive Rate
Tru
e P
o
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Instituto Telecomunicaciones y Aplicaciones Multimedia
0 50 100 150 200 250 300 350 400 450 500-1
0
1
0 50 100 150 200 250 300 350 400 450 500-2
0
2
0 50 100 150 200 250 300 350 400 450 500-0.5
0
0.5
0 50 100 150 200 250 300 350 400 450 500-0.5
0
0.5
0
0.5
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 10
2000
4000
-6 -5 -4 -3 -2 -1 0 10
5000
10000
-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 30
5000
-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.60
5000
2000
4000
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.1
0.2
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.05
0.1
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.020.040.06
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.020.04
0
0.02
0.04
-20 -15 -10 -5 0 5 10 15 20-0.1
0
0.1
-20 -15 -10 -5 0 5 10 15 20-0.05
0
0.05
-20 -15 -10 -5 0 5 10 15 20-0.02
0
0.02
-20 -15 -10 -5 0 5 10 15 20-0.1
0
0.1
0
0.02
Surrogate data from legitimate operations
0 50 100 150 200 250 300 350 400 450 500-0.5
0 50 100 150 200 250 300 350 400 450 500-0.5
0
0.5
0 50 100 150 200 250 300 350 400 450 500-0.5
0
0.5
0 50 100 150 200 250 300 350 400 450 500-0.5
0
0.5
-1 -0.5 0 0.50
-0.4 -0.2 0 0.2 0.4 0.6 0.8 10
5000
10000
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.60
5000
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.60
5000
10000
-10 -8 -6 -4 -2 0 2 4 6 8 10
0
-10 -8 -6 -4 -2 0 2 4 6 8 10
00.010.020.03
-10 -8 -6 -4 -2 0 2 4 6 8 10
00.010.02
-10 -8 -6 -4 -2 0 2 4 6 8 10
00.010.02
-20 -15 -10 -5 0 5 10 15 20-0.02
-20 -15 -10 -5 0 5 10 15 20-0.05
0
0.05
-20 -15 -10 -5 0 5 10 15 20-0.02
0
0.02
-20 -15 -10 -5 0 5 10 15 20-0.1
0
0.1
500 surrogate samples Histograms (real and surro) Autocorrelation comparison Cross‐correlations
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Surrogate data from fraud operations
0 50 100 150 200 250 300 350 400 450 500-0.2
0
0.2
0 50 100 150 200 250 300 350 400 450 500-2
0
2
0 50 100 150 200 250 300 350 400 450 500-0.5
0
0.5
0 50 100 150 200 250 300 350 400 450 500-1
0
1
1
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.20
200
400
-6 -5 -4 -3 -2 -1 0 10
500
1000
-1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.60
200
400
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.60
100
200
400
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.10.2
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.050.1
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.05
0.1
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.020.040.06
-20 -15 -10 -5 0 5 10 15 20-0.05
0
0.05
-20 -15 -10 -5 0 5 10 15 20-0.02
0
0.02
-20 -15 -10 -5 0 5 10 15 20-0.05
0
0.05
-20 -15 -10 -5 0 5 10 15 20-0.05
0
0.05
002
500 surrogate samples Histograms (real and surro) Autocorrelation comparison Cross‐correlations
0 50 100 150 200 250 300 350 400 450 500-1
0
1
0 50 100 150 200 250 300 350 400 450 500-1
0
1
0 50 100 150 200 250 300 350 400 450 500-1
0
1
0 50 100 150 200 250 300 350 400 450 500-1
0
1
-1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.40
200
400
-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.80
200
400
-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.80
200
400
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.60
100
200
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.020.04
-10 -8 -6 -4 -2 0 2 4 6 8 100
0.020.04
-10 -8 -6 -4 -2 0 2 4 6 8 10
0
0.02
0.04
-10 -8 -6 -4 -2 0 2 4 6 8 10
00.010.020.03
-20 -15 -10 -5 0 5 10 15 20-0.02
0
0.02
-20 -15 -10 -5 0 5 10 15 20-0.01
0
0.01
-20 -15 -10 -5 0 5 10 15 20-0.01
0
0.01
-20 -15 -10 -5 0 5 10 15 20-0.2
0
0.2
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Addisson Salazar, Univ. Politècnica de València 7
Instituto Telecomunicaciones y Aplicaciones Multimedia
Surrogate joint distributions
-0.5 0 0.5
-0.6-0.4-0.2
00.20.4
-0.5 0 0.5
-0.6-0.4-0.2
00.20.4
-0.5 0 0.5
-0.4-0.2
00.2
-0.5 0 0.5
-0.4-0.2
00.2
-0.5 0 0.5
-0.4-0.2
0
0.2
-0.5 0 0.5
-0.4-0.2
0
0.2
-0.5 0 0.5
-0.2
0
0.2
-0.5 0 0.5
-0.2
0
0.2
-0.05 0 0.05
-0.2
0
0.2
-0.05 0 0.05
-0.2
0
0.2
-0.1 0 0.1
-0.2
0
0.2
0.4-0.1 0 0.1
-0.2
0
0.2
0.4
-0.1 -0.05 0 0.05
-0.4-0.2
00.2
-0.1 -0.05 0 0.05
-0.4-0.2
00.2
-0.1 0 0.1
-0.4
-0.2
0
0.2
-0.1 0 0.1
-0.4
-0.2
0
0.2
0 4 0 4 0 4 0 4
Comparison of Legitimate operation joint distribution Comparison of Fraud operation joint distribution
-0.5 0 0.5
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0
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0
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-0.5 0 0.5
-0.2-0.10
0.1
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0.1
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-0.2-0.10
0.1
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0
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2
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0
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2
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-0.4-0.2
00.20.4
-0.1 -0.05 0 0.05
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00.20.4
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00.20.40.6
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00.20.40.6
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Instituto Telecomunicaciones y Aplicaciones Multimedia
0 5
0.6
0.7
0.8
0.9
1
sitiv
e R
ate
Amount
of
surrogate
data
AUC calculated on the:
Full ROC
curves
Zoom in
the
detection
zone of
interest
0% 0 8708 0 0656
ROC curves: real and surrogates
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
False Positive Rate
Tru
e P
os
Real dataSurrogates 100%Surrogates 50%Surrogates 75%
0% 0.8708 0.0656 50% 0.8641 0.0640 75% 0.8563 0.0591 100% 0.8678 0.0589
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0.4
0.5
0.6
0.7
itive
Rat
e
ROC curves in the zone of interest
0 0.02 0.04 0.06 0.08 0.10
0.1
0.2
0.3
False Positive Rate
Tru
e P
osi
Real dataSurrogates 100%Surrogates 50%Surrogates 75%
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Kind of feature Feature
Amplitude Average amplitude
Maximum amplitude
Average power
Spectral Centroid frequency
Maximum frequency
Spindles ratio
TSI
ASI
Statistical Skewness
Kurtosis
Apnea (microarousal detection)
Time reversibility
Third‐order self‐covariance
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Apnea (SICAMM paremeters)
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Apnea (SICAMM paremeters)
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Addisson Salazar, Univ. Politècnica de València 8
Instituto Telecomunicaciones y Aplicaciones Multimedia
EEGsignal
capture
TestVisual stimuli
Audio stimuli
Neuropsychological Tests
Signalprocessing
analysis
Time
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Me
mo
rize
Me
mo
rize
Re
spo
nse
Re
spo
nse
Re
spo
nse
F3
F1
AF3
AF7
Fp1
Neuropsychological Tests
Time (s)
33343536373839404142434445464748495051525354555657585960616263C3
C1
FC1
FC3
FC5
FT7
F7
F5
F3
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Instituto Telecomunicaciones y Aplicaciones Multimedia
Neuropsychological Tests
0 20 40 60 80 100
True dataICAMM
SICAMMSICAM+BW
SICAMM+VIG-SICAMM
G-SICAMM+BWG-SICAMM+VI
BNTDBN
DBN2
0 200 400 600 800
TAVECSubject #5
Figural MemorySubject #4
a) Time from the start of the test (s) b) Time from the start of the test (s)
0 100 200 300 400
True dataICAMM
SICAMMSICAM+BW
SICAMM+VIG-SICAMM
G-SICAMM+BWG-SICAMM+VI
BNTDBN
DBN2
c) Time from the start of the test (s)0 200 400 600 800
d) Time from the start of the test (s)
Verbal Paired AssociatesSubject #5
TAVECSubject #6
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Neuropsychological Tests
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ContentsContents
ReferencesReferences
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Instituto Telecomunicaciones y Aplicaciones Multimedia
ReferencesReferences
JCR Journals• Vergara L., Soriano A., Safont G., Salazar A., On the fusion of non‐independent detectors, Digital Signal Processing, vol. 50, pp. 24‐33, 2016.• Safont G., Salazar A., Vergara L., Probabilistic Distance for Mixtures of Independent Component Analyzers, submitted to IEEE Transactions on Neural Networks and Learning Systems, 2016.• Safont G., Salazar A., Vergara L., Gomez E., Villanueva V., Multichannel Dynamic Modeling of Non‐Gaussian Mixtures, submitted to IEEE Transactions on Neural Networks and Learning Systems, 2016.• Igual J, Salazar A., Safont A., Vergara L., Semi‐supervised Bayesian classification of materials with impact‐echo signals, Sensors, vol. 15 no. 5, pp. 11528‐11550, 2015.• Soriano A., Vergara L., Bouziane A., Salazar A., Fusion of Scores in a Detection Context Based on Alpha Integration, Neural Computation, vol. 27 no. 9, pp. 1983‐2010, 2015.g p pp• Safont G., Salazar A., Rodriguez A., Vergara L., New prediction methods based on Independent Component Analyzers Mixture Models, submitted to Signal Processing, 2015.• Safont G., Salazar A., Rodriguez A., Vergara L., On Recovering Missing GPR Traces by Statistical Interpolation Methods, Remote Sensing, 6, pp. 7546‐7565, 2014.• Rodriguez A., Salazar A., Vergara L., Analysis of split‐spectrum algorithms in an automatic detection framework, Signal Processing, vol. 92, pp. 2293–2307, 2012.• Llinares R., Igual J., Salazar A., Camacho A., Semi‐blind source extraction of atrial activity by combining statistical and spectral features, Digital Signal Processing, vol. 21 no. 2, pp. 391‐403, 2011.• Salazar A., Vergara L., Serrano A., Igual J., A General Procedure for Learning Mixtures of Independent Component Analyzers, Pattern Recognition, vol. 43 no. 1, pp. 69‐85, 2010.• Salazar A., Vergara L., Miralles R., On including sequential dependence in ICA mixture models, Signal Processing, vol. 90, pp. 2314‐2318, 2010.
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Addisson Salazar, Univ. Politècnica de València 9
Instituto Telecomunicaciones y Aplicaciones Multimedia
ReferencesReferences• Salazar A., Vergara L., Llinares R., Learning Material Defect Patterns by Separating Mixtures of Independent Component Analyzers from NDT Sonic Signals, Mechanical Systems and Signal Processing, vol. 24 no. 6, pp. 1870‐1886, 2010.• Salazar A., Vergara L., ICA Mixtures Applied to Ultrasonic Non‐destructive Classification of Archaeological Ceramics, Journal on Advances in Signal Processing, vol. 2010, Article ID 125201, 11 pages, doi:10.1155/2010/125201, 2010.• Vergara L., Moragues J. Gosalbez J., Salazar A., Detection of signals of unknown duration by multiple energy detectors, Signal Processing, vol. 90, pp. 719‐726, 2010.
Books and Book Chapters
• Salazar A., On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling, Springer‐Verlag, Berlin, 2013.• Safont G., Salazar A., Rodriguez A., Vergara L., An Experimental Sensitivity Analysis of Gaussian and Non‐Gaussian based Methods for Dynamic Modeling in EEG Signal Processing, In Encyclopedia of Information Science and Technology, Third Edition, IGI Global, pp. 4028‐404, USA, 2014.• Salazar A., Vergara L., Perspectives on Pattern Recognition from ICA Mixture Modeling, in "Perspectives on Pattern Recognition", Nova Science Publishers, Inc., pp. 203‐223, USA, 2011.• Salazar A., Vergara L., Knowledge Discovery from E‐Learning Activities, in "Advances in E‐Learning: Experiences and Methodologies", IGI‐Global, pp. 173‐198, USA, 2008.
International Conferences• Salazar A., Igual J., Safont G., Vergara L., Vidal A., Image applications of agglomerative clustering using mixtures of non‐Gaussian distributions, CSCI 2015, Int. Conf. on Comp. Sci. Comp. Intell., pp. 459‐463, USA, 2015.
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ReferencesReferences• Salazar A., Igual J., Vergara L., Agglomerative Clustering of Defects in Ultrasonic Non‐destructive Testing using Hierarchical Mixtures of Independent Component Analyzers, IEEE 2014 International Joint Conference on Neural Networks, IJCNN, pp. 2042‐2049, Beijing, China, 2014.• Salazar A., Safont G., Vergara L., Surrogate techniques for testing fraud detection algorithms in credit card operations, 48th IEEE International Carnahan Conference on Security Technology, IEEE ICCST, pp. 1‐6, Rome, Italy, 2014.• Safont G., Salazar A., Vergara L., Gomez E., Villanueva V., Mixtures of Independent Component Analyzers for Microarousal Detection, IEEE Second International Conference on Biomedical and Health Informatics (BHI 2014), pp. 752‐755, Valencia, Spain, 2014. • Safont G., Salazar A., Vergara L., Vidal A., Gonzalez A., Assessment of historic structures based on GPR, ultrasound, and impact‐echo data fusion, Key Engineering Materials, vol. 569‐570, pp. 1210‐1217, Dublin, 2013.
S i S f G S l O i h d d f f i f d d d• Soriano A., Vergara L., Safont G., Salazar A., On comparing hard and soft fusion of dependent detectors, Proceedings ‐ IEEE Int. Works.on Mach.Learn. for Sig. Proc., MLSP 2012, art no. 6349792, pp. 1‐6, Santander, 2012.• Safont G., Salazar A., Vergara L., Gonzalez A., Vidal A., Mixtures of independent component analyzers for EEG prediction, Communications in Computer and Information Science, vol. 338 CCIS, pp. 328‐335, 2012.• Salazar A., Safont G., Soriano A., Vergara L., Automatic Credit Card Fraud Detection based on Non‐linear Signal Processing, Proceedings ‐ International Carnahan Conference on Security Technology 2012, art no. 6393560, pp. 207‐212, Boston, USA, 2012.• Safont G., Salazar A., Soriano A., Vergara L., Combination of Multiple Detectors for EEG based Biometric Identification/Authentication, Proceedings ‐ International Carnahan Conference on Security Technology 2012, art no. 6393564, pp. 230‐236, Boston, USA, 2012.• Salazar A., Gosalbez J., Safont G., Vergara L., Data Fusion of Ultrasound and GPR Signals for Analysis of Historic Walls, Proceedings of International Simposium on Ultrasounds in the Control of Industrial Processes, UCIP 2012, IOP Conference Series: Materials Science and Engineering, Madrid, Spain, 2012.
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