Feature extraction for change detection Can you detect an abrupt change in this picture? Ludmila I...
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Feature extraction for change detection Can you detect an abrupt change in this picture? Ludmila I Kuncheva School of Computer Science Bangor University
Feature extraction for change detection Can you detect an
abrupt change in this picture? Ludmila I Kuncheva School of
Computer Science Bangor University Answer at the end
Slide 3
Plan 1.Zeno says there is no such thing as change... 2.If
change exists, is it a good thing? 3.Context or nothing! 4.Feature
extraction for change detection PCA backwards?
Slide 4
Zeno of Elea (ca. 490430 BC) If everything, when it occupies an
equal space, is at rest, and if that which is in locomotion is
always occupying such a space at any moment, the flying arrow is
therefore motionless. as recounted by Aristotle, Physics VI:9,
239b5 No motion, no movement, NO CHANGE Zenos Paradox of the
Arrow
Slide 5
Does change exist? Zeno says no...
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Nonetheless... Change Types Possible applications: fraud
detection market analysis medical condition monitoring network
traffic control Univariate detectors (Control charts): Shewhart's
method CUSUM (CUmulative SUM) SPRT (Wald's Sequential Probability
Ratio Test)
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2 approaches Use an adaptive algorithm (No need to identify the
type of change or detect change explicitly) Detect change
(Update/re-train the algorithm if necessary) Labelled data
Unlabelled data
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Data (all features) Labels are available Classifier
Distribution modelling Error rate Change statistic threshold
Change/ NO change Classification
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Data (all features) Labels are available Labels are NOT
available Classifier Distribution modelling Error rate Change
statistic threshold Change/ NO change Data (all features) Feature
EXTRACTOR Distribution modelling Change statistic threshold Change/
NO change Features multidimensional
Slide 10
Data (all features) Labels are available Labels are NOT
available Classifier GMM HMM Parzen windows kernel methods
martingales Error rate threshold Change/ NO change Data (all
features) Feature EXTRACTOR clustering kernel methods GMM kd-trees
Hotelling threshold Change/ NO change Features
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A change in the (unconditional) data distribution will:
1.render the classifier useless 2.make no difference to the
classification performance 3.improve the classification performance
Classification
Slide 12
A change in the (unconditional) data distribution will:
1.render the classifier useless 2.make no difference to the
classification performance 3.improve the classification performance
Vote, please!
Slide 13
A change in the (unconditional) data distribution will:
1.render the classifier useless 2.make no difference to the
classification performance 3.improve the classification performance
Vote, please!
Slide 14
Classification No change in the (unconditional) data
distribution will: 1.render the classifier useless 2.make no
difference to the classification performance 3.improve the
classification performance
Slide 15
No change in the (unconditional) data distribution will:
1.render the classifier useless 2.make no difference to the
classification performance 3.improve the classification performance
Vote, please!
Slide 16
Classifier ensembles Brain-computer interface MathWorks
products My scope of interest Literature
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Change may or may not cause trouble...
Slide 18
Is there a change ?
Slide 19
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mean (moving average) mean 2std changes Shewhart with threshold
2 sigma Yes!
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Is there a change ? No!
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Is there a change?
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Yes, for the purposes of Spot the difference. No, as this is a
bee with a flower in the sun.
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Is there a change? No!
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Is there a change? sin(10x) * randn sin(20x) * randn Yes!
Slide 26
change detection
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Change does not exist out of context!
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ENTER Feature Extraction
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Context: Amplitude variability Feature: AMPLITUDE
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Context: Time series patterns in a fixed window. Feature: A
PATTERN IN A FIXED WINDOW
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Context: Childrens puzzle Feature: PIXEL B/W VALUE Context:
Frequency variability Feature: FREQUENCY sin(10x) * randnsin(20x) *
randn
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Suppose that CONTEXT is not available. Principal Component
Analysis (PCA) captures data variability. Then why not use PCA
here? Labels are NOT available Data (all features) Feature
EXTRACTOR Distribution modelling Change statistic threshold Change/
NO change Features
Slide 34
PCA intuition: The components corresponding to the largest
eigen values are more important
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But is this the case for change detection? Distributions are
similar (small sensitivity to change) Distributions are different
(large sensitivity to change) PC1 PC2 Holds for blind: Translation
Rotation Variance change... Kuncheva L.I. and W.J. Faithfull, PCA
feature extraction for change detection in multidimensional
unlabelled data, IEEE Transactions on Neural Networks and Learning
Systems, 25(1), 2014, 69-80
Slide 36
Some experiments: 1.Take a data set with n features 2.Sample
randomly windows W1 and W2 with K objects in each window.
3.Calculate PCA from W1. Choose a proportion of explained variance
and use the remaining (low- variance) components. 4.Generate a
random integer k between 1 and n 4(a)Shuffle VALUES Choose randomly
k features. For each chosen feature, shuffle randomly the values
for this feature in window W2. 4(b)Shuffle FEATURES Choose randomly
k features. Randomly permute the respective columns in window W2.
5.Transform W2 using the calculated PC and keep the low-variance
components. 6.Calculate the CHANGE DETECTION CRITERION between W1
and W2. Store as NEGATIVE INSTANCE (no change).
Slide 37
Some experiments: 1.Take a data set with n features 2.Sample
randomly windows W1 and W2 with K objects in each window.
3.Calculate PCA from W1. Choose a proportion of explained variance
and use the remaining (low- variance) components. 4.Generate a
random integer k between 1 and n 4(a)Shuffle VALUES Choose randomly
k features. For each chosen feature, shuffle randomly the values
for this feature in window W2. 4(b)Shuffle FEATURES Choose randomly
k features. Randomly permute the respective columns in window W2.
5.Transform W2 using the calculated PC and keep the low-variance
components. 6.Calculate the CHANGE DETECTION CRITERION between W1
and W2. Store as POSITIVE INSTANCE (change).
Slide 38
Run 100 times for POS and 100 for NEG to get the ROC curve for
a given data set. Run 100 times for POS and 100 for NEG without
applying PCA to get the ROC curve for a given data set. Use the
Area Under the Curve (AUC), however disputed this might have become
recently... Larger AUC corresponds to better change detection
Slide 39
VALUE shuffle
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FEATURE shuffle
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PCA - use the least relevant components!?
Slide 44
Conclusion 1.Change detection may be harmful, beneficial or
indifferent to classification performance 2.Change does not exist
out of context, therefore GENERIC algorithms for change detection
are somewhat pointless... 3.Feature extraction for change detection
may not follow conventional intuition.
Slide 45
1-3 4-6 Can you detect an abrupt change in this picture?
Remember my little puzzle?
