Anomaly detection (also known as outlier analysis) is a data mining technique used to identify data points, events, or observations that deviate significantly from a dataset’s normal behavior. These anomalies can signify critical incidents, such as technical glitches, or potential opportunities, such as shifts in consumer behavior. Machine learning increasingly automates the process of anomaly detection to improve accuracy and efficiency.
Autoencoder-based anomaly detection leverages autoencoders to identify anomalies by compressing normal data into a smaller latent space and then reconstructing it. The autoencoder consists of two main parts:
- Encoder: Compresses the normal data into a lower-dimensional latent space.
- Decoder: Reconstructs the compressed data back to its original dimension.
The autoencoder learns to minimize the difference between the original and reconstructed data, which helps in effectively capturing the features of normal data in the latent space.
Anomaly detection using autoencoders relies on the concept of reconstruction error, which measures the difference between the original and reconstructed data. Since the autoencoder is trained to reconstruct normal data effectively, anomalies—data points that deviate significantly from the norm—will have a higher reconstruction error. This error serves as an anomaly score, and if it exceeds a predefined threshold, the data is classified as anomalous. Thresholds may vary based on specific requirements and characteristics of the anomaly detection task.
An autoencoder learns to produce an output as close as possible to the input. Through this learning process, it effectively compresses the input into a lower-dimensional latent space while preserving essential information.
Train the autoencoder using only the normal data labeled as 1 in this dataset. This involves separating normal and abnormal events to ensure that the model learns to recognize and reconstruct only the normal patterns.