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kategorie
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pojęcia
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Curse of Dimensionality

Phenomenon where data becomes sparse and difficult to analyze when the number of dimensions increases exponentially, making traditional anomaly detection methods ineffective.

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Variational Autoencoders

Deep neural networks that learn a compressed representation of normal data, where anomalies present high reconstruction errors due to their low probability in the latent space.

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Robust PCA

Variant of Principal Component Analysis resistant to outliers, using robust estimators to identify anomalies without them influencing the dimensional decomposition.

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Subspace Clustering

Approach detecting anomalies in different dimensional subspaces, where a point can be normal in certain dimensions but abnormal in other combinations.

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Deep SVDD

Extension of Support Vector Data Description using deep neural networks to learn a compact enclosing sphere of normal data in a non-linear feature space.

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Projection Pursuit

Method seeking data projections that reveal interesting structures or anomalies, particularly useful for detecting non-obvious patterns in high dimension.

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Random Projection

Dimensionality reduction technique that approximately preserves distances between points, used to accelerate anomaly detection while maintaining geometric properties.

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DAGMM

Deep Autoencoding Gaussian Mixture Model combining autoencoders and Gaussian mixture models to capture complex distributions and detect anomalies in very high dimensional spaces.

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