Dictionary Learning for Anomaly Detection | Data Science Interview Questions | Machine Learning

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Dictionary Learning for Anomaly Detection | Data Science Interview Questions | Machine Learning

Dictionary Learning is a technique that learns a sparse representation of the original data using a set of vectors called a dictionary. And then the anomalous data points are the ones having significantly different representation.

First, a dictionary is learned from a set of training data points. This dictionary is typically overcomplete, meaning it has more atoms than the dimensionality of the data. The atoms are learned using a method such as the K-SVD algorithm.

Each data point is then represented as a sparse linear combination of the atoms in the learned dictionary, using methods such as orthogonal matching pursuit.

The reconstruction error is calculated as the difference between the original data point and its representation using the learned dictionary.

Anomalous data points are identified as those with a reconstruction error that exceeds a certain threshold.

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