Anomaly Detection

β

β

β

β

- 2.
- 3.
- 4.
- 5.
- 6.
- 8.
**A****comparison****of One-class SVM versus Elliptic Envelope versus Isolation Forest versus LOF in sklearn. (The examples below illustrate how the performance of the****covariance.EllipticEnvelope****degrades as the data is less and less unimodal. The****svm.OneClassSVM****works better on data with multiple modes and****ensemble.IsolationForest****and****neighbors.LocalOutlierFactor****perform well in every cases.)** - 9.
- 10.
**Twitter anomaly -** - 11.
**Microsoft anomaly - a well documented black box, i cant find a description of the algorithm, just hints to what they sort of did**- 2.

- 12.
- 1.

- 1.β
**Alibi Detect****is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. The outlier detection methods should allow the user to identify global, contextual and collective outliers.** - 2.

βββ

- 1.
- 3.β
**SUOD****(Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction. Notably, anomaly detection is often formulated as an unsupervised problem since the ground truth is expensive to acquire. To compensate for the unstable nature of unsupervised algorithms, practitioners often build a large number of models for further combination and analysis, e.g., taking the average or majority vote. However, this poses scalability challenges in high-dimensional, large datasets, especially for proximity-base models operating in Euclidean space.**

- 1.
- 2.
- 1.ββ
- 2.ββ

- 1.ββ

β

β**The best resource to explain isolation forest**** - the basic idea is that for an anomaly (in the example) only 4 partitions are needed, for a regular point in the middle of a distribution, you need many many more.
**

**randomly selecting a feature****randomly selecting a split value between the maximum and minimum values of the selected feature.**

- β
**LOF****computes a score (called local outlier factor) reflecting the degree of abnormality of the observations.** **It measures the local density deviation of a given data point with respect to its neighbors. The idea is to detect the samples that have a substantially lower density than their neighbors.****In practice the local density is obtained from the k-nearest neighbors.****The LOF score of an observation is equal to the ratio of the average local density of his k-nearest neighbors, and its own local density:****a normal instance is expected to have a local density similar to that of its neighbors,****while abnormal data are expected to have much smaller local density.**

- 1.
**We assume that the regular data come from a known distribution (e.g. data are Gaussian distributed).** - 2.
**From this assumption, we generally try to define the βshapeβ of the data,** - 3.
**And can define outlying observations as observations which stand far enough from the fit shape.**

- 2.
- 3.
**It looks like there are****two such methods****, - The 2nd one: The algorithm obtains a spherical boundary, in feature space, around the data. The volume of this hypersphere is minimized, to minimize the effect of incorporating outliers in the solution.**

- 1.
- 3.β
**A notebook****, using the SuperHeat package, clustering w2v cosine similarity matrix, measuring using silhouette score.**

Last modified 4mo ago