Incremental Principal Component Analysis Exact implementation and continuity corrections
This work addresses the need for efficient, memory-saving PCA in streaming data applications, but it is incremental as it builds on existing PCA methods with a focus on continuity corrections.
The paper tackles the problem of performing Principal Component Analysis (PCA) in real-time by introducing an incremental algorithm that updates transformation coefficients for each new sample without storing all data, achieving exact equivalence to batch PCA results.
This paper describes some applications of an incremental implementation of the principal component analysis (PCA). The algorithm updates the transformation coefficients matrix on-line for each new sample, without the need to keep all the samples in memory. The algorithm is formally equivalent to the usual batch version, in the sense that given a sample set the transformation coefficients at the end of the process are the same. The implications of applying the PCA in real time are discussed with the help of data analysis examples. In particular we focus on the problem of the continuity of the PCs during an on-line analysis.