The Data Science Lab Anomaly Detection Using Principal Component Analysis (PCA) The main advantage of using PCA for anomaly detection, compared to alternative techniques such as a neural autoencoder, ...

Principal component analysis is a very complex decomposition that works on data matrices instead of single integer values. In my opinion, PCA is best understood by examining a concrete example, such ...

Principal Component Analysis (PCA) is widely used in data analysis and machine learning to reduce the dimensionality of a dataset. The goal is to find a set of linearly uncorrelated (orthogonal) ...

Plant Ecology, Vol. 216, No. 5, Special Issue: Statistical Analysis of Ecological Communities: Progress, Status, and Future Directions (MAY 2015), pp. 657-667 (11 pages) Principal component analysis ...

It emerges that representations using the proposed derivative principal component analysis recover the underlying derivatives more accurately compared to principal component analysis-based approaches ...