PCA: Principal Component Analysis

PCA (Principal Component Analysis) is an unsupervised machine learning algorithm used to reduce the dimensionality of the given data. It has first been invented by Karl Pearson (1901) and independently developed by Harold Hotelling (1933). Dimensionality reduction refers to the mapping of the original high-dimensional data onto a lower-dimensional space, thus reducing the risk of model overfitting and improving the generalization ability of the model … Continue reading PCA: Principal Component Analysis

Log analysis for anomaly detection

Anomaly detection plays an important role in the management of modern large-scale distributed systems. Logs are widely used for anomaly detection, recording system runtime information, and errors. Traditionally, operators have to go through the logs manually with keyword searching and rule matching. The increasing scale and complexity of modern systems, however, make the volume of logs explode, which renders the infeasibility of manual inspection. To … Continue reading Log analysis for anomaly detection